pascal perez

Quantification of visual blockage at culverts using deep learning based computer vision models

Publisher: Informa UK Limited

Date: 21-10-2022

DOI: 10.1080/1573062X.2022.2134041

Managing hydrological infrastructure assets for improved flood control in coastal mega-cities of developing nations

Publisher: Elsevier BV

Date: 06-2018

DOI: 10.1016/J.UCLIM.2017.09.002

Assessing the effects of mobility on air quality: The Liverpool Smart Pedestrian project

Publisher: Elsevier BV

Date: 2020

DOI: 10.1016/J.TRPRO.2020.08.276

V2ReID: Vision-Outlooker-Based Vehicle Re-Identification

Publisher: MDPI AG

Date: 09-11-2022

DOI: 10.3390/S22228651

Abstract: With the increase of large camera networks around us, it is becoming more difficult to manually identify vehicles. Computer vision enables us to automate this task. More specifically, vehicle re-identification (ReID) aims to identify cars in a camera network with non-overlapping views. Images captured of vehicles can undergo intense variations of appearance due to illumination, pose, or viewpoint. Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. These are, however, not always available or can be resource intensive for the model. Following the success of Transformer baselines in ReID, we propose for the first time an outlook-attention-based vehicle ReID framework using the Vision Outlooker as its backbone, which is able to encode finer-level features. We show that, without embedding any additional side information and using only the visual cues, we can achieve an 80.31% mAP and 97.13% R-1 on the VeRi-776 dataset. Besides documenting our research, this paper also aims to provide a comprehensive walkthrough of vehicle ReID. We aim to provide a starting point for in iduals and organisations, as it is difficult to navigate through the myriad of complex research in this field.

A process-driven and need-oriented framework for review of technological contributions to disaster management

Publisher: Elsevier BV

Date: 11-2021

DOI: 10.1016/J.HELIYON.2021.E08405

A review of structural approach to flood management in coastal megacities of developing nations: current research and future directions

Publisher: Informa UK Limited

Date: 14-02-2019

DOI: 10.1080/09640568.2018.1547693

Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review

Publisher: MDPI AG

Date: 2023

DOI: 10.3390/DRONES7010032

Abstract: Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities.

Understanding spatial variability of air quality in Sydney: Part 2-a roadside case study

Publisher: MDPI AG

Date: 23-04-2019

DOI: 10.3390/ATMOS10040217

Abstract: Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road section with ambient regional urban background levels, as measured at nearby regulatory air quality stations. The study also explored spatial and temporal variations in the observed PM2.5 concentrations. The chosen area was Randwick in Sydney, because it was also the subject area for an agent-based traffic model. Over a four-day c aign in February 2017, continuous measurements of PM2.5 were made along and around the main road. In addition, a traffic counting application was used to gather data for evaluation of the agent-based traffic model. The average hourly PM2.5 concentration was 13 µg/m3, which is approximately twice the concentrations at the nearby regulatory air quality network sites measured over the same period. Roadside concentrations of PM2.5 were about 50% higher in the morning rush-hour than the afternoon rush hour, and slightly lower (reductions of %) 50 m away from the main road, on cross-roads. The traffic model under-estimated vehicle numbers by about 4 fold, and failed to replicate the temporal variations in traffic flow, which we assume was due to an influx of traffic from outside the study region dominating traffic patterns. Our findings suggest that those working for long hours outdoors at busy roadside locations are at greater risk of suffering detrimental health effects associated with higher levels of exposure to PM2.5. Furthermore, the worse air quality in the morning rush hour means that, where possible, joggers and cyclists should avoid busy roads around these times.

An Information Management Strategy for City Data Hubs: Open Data Strategies for Large Organisations

Publisher: Springer Nature Singapore

Date: 27-09-2019

DOI: 10.1007/978-981-13-6605-5_13

A Scaled Physical Model Study of Culvert Blockage Exploring Complex Relationships Between Influential Factors

Publisher: Informa UK Limited

Date: 14-11-2023

DOI: 10.1080/13241583.2021.1996679

Investigating the Relationship Between Students’ Preferred Learning Style on Their Learning Experience in Virtual Reality (VR) Learning Environment

Publisher: Springer International Publishing

Date: 25-07-2019

DOI: 10.1007/978-3-030-25629-6_43

Investigating the process of mine rescuers' safety training with immersive virtual reality: A structural equation modelling approach

Publisher: Elsevier BV

Date: 08-2020

DOI: 10.1016/J.COMPEDU.2020.103891

Urban Vehicle Localization in Public LoRaWan Network

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 15-06-2022

DOI: 10.1109/JIOT.2021.3121778

Examining the potential of virtual reality to deliver remote rehabilitation

Publisher: Elsevier BV

Date: 04-2020

DOI: 10.1016/J.CHB.2019.106223

Floodborne Objects Type Recognition Using Computer Vision to Mitigate Blockage Originated Floods

Publisher: MDPI AG

Date: 24-08-2022

DOI: 10.3390/W14172605

Abstract: The presence of floodborne objects (i.e., vegetation, urban objects) during floods is considered a very critical factor because of their non-linear complex hydrodynamics and impacts on flooding outcomes (e.g., ersion of flows, damage to structures, downstream scouring, failure of structures). Conventional flood models are unable to incorporate the impact of floodborne objects mainly because of the highly complex hydrodynamics and non-linear nature associated with their kinematics and accumulation. Vegetation (i.e., logs, branches, shrubs, entangled grass) and urban objects (i.e., vehicles, bins, shopping carts, building waste materials) offer significant materialistic, hydrodynamic and characterization differences which impact flooding outcomes differently. Therefore, recognition of the types of floodborne objects is considered a key aspect in the process of assessing their impact on flooding. The identification of floodborne object types is performed manually by the flood management officials, and there exists no automated solution in this regard. This paper proposes the use of computer vision technologies for automated floodborne objects type identification from a vision sensor. The proposed approach is to use computer vision object detection (i.e., Faster R-CNN, YOLOv4) models to detect a floodborne object’s type from a given image. The dataset used for this research is referred to as the “Floodborne Objects Recognition Dataset (FORD)” and includes real images of floodborne objects blocking the hydraulic structures extracted from Wollongong City Council (WCC) records and simulated images of scaled floodborne objects blocking the culverts collected from hydraulics laboratory experiments. From the results, the Faster R-CNN model with MobileNet backbone was able to achieve the best Mean Average Precision (mAP) of 84% over the test dataset. To demonstrate the practical use of the proposed approach, two potential use cases for the proposed floodborne object type recognition are reported. Overall, the performance of the implemented computer vision models indicated that such models have the potential to be used for automated identification of floodborne object types.

SD-seq2seq : A Deep Learning Model for Bus Bunching Prediction Based on Smart Card Data

Publisher: IEEE

Date: 08-2020

DOI: 10.1109/ICCCN49398.2020.9209686

Green infrastructure for air quality improvement in street canyons

Publisher: Elsevier BV

Date: 2021

DOI: 10.1016/J.ENVINT.2020.106288

A qualitative evaluation of the role of virtual reality as a safety training tool for the mining industry

Publisher: Springer International Publishing

Date: 2018

DOI: 10.1007/978-3-319-78795-4_14

Autonomous Lidar-Based Monitoring of Coastal Lagoon Entrances

Publisher: MDPI AG

Date: 30-03-2021

DOI: 10.3390/RS13071320

Abstract: Intermittently closed and open lakes or Lagoons (ICOLLs) are characterised by entrance barriers that form or break down due to the action of wind, waves and currents until the ocean-lagoon exchange becomes discontinuous. Entrance closure raises a variety of management issues that are regulated by monitoring. In this paper, those issues are investigated, and an automated sensor solution is proposed. Based upon a static Lidar paired with an edge computing device. This solar-powered remote sensing device provides an efficient way to automatically survey the lagoon entrance and estimate the berm profile. Additionally, it estimates the dry notch location and its height, critical factors in the management of the lagoon entrances. Generated data provide valuable insights into landscape evolution and berm behaviour during natural and mechanical breach events.

A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network

Publisher: Springer Science and Business Media LLC

Date: 06-2018

DOI: 10.1007/S10479-018-2911-2

An Architecture Framework Approach for Complex Transport Projects

Publisher: Springer Science and Business Media LLC

Date: 02-01-2020

DOI: 10.1007/S10796-019-09978-Y

The factors affecting the quality of learning process and outcome in virtual reality environment for safety training in the context of mining industry

Publisher: Springer International Publishing

Date: 27-06-2019

DOI: 10.1007/978-3-319-94223-0_38

Automating Visual Blockage Classification of Culverts with Deep Learning

Publisher: MDPI AG

Date: 18-08-2021

DOI: 10.3390/APP11167561

Abstract: Blockage of culverts by transported debris materials is reported as the salient contributor in originating urban flash floods. Conventional hydraulic modeling approaches had no success in addressing the problem primarily because of the unavailability of peak floods hydraulic data and the highly non-linear behavior of debris at the culvert. This article explores a new dimension to investigate the issue by proposing the use of intelligent video analytics (IVA) algorithms for extracting blockage related information. The presented research aims to automate the process of manual visual blockage classification of culverts from a maintenance perspective by remotely applying deep learning models. The potential of using existing convolutional neural network (CNN) algorithms (i.e., DarkNet53, DenseNet121, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, VGG16, EfficientNetB3, NASNet) is investigated over a dataset from three different sources (i.e., images of culvert openings and blockage (ICOB), visual hydrology-lab dataset (VHD), synthetic images of culverts (SIC)) to predict the blockage in a given image. Models were evaluated based on their performance on the test dataset (i.e., accuracy, loss, precision, recall, F1 score, Jaccard Index, region of convergence (ROC) curve), floating point operations per second (FLOPs) and response times to process a single test instance. Furthermore, the performance of deep learning models was benchmarked against conventional machine learning algorithms (i.e., SVM, RF, xgboost). In addition, the idea of classifying deep visual features extracted by CNN models (i.e., ResNet50, MobileNet) using conventional machine learning approaches was also implemented in this article. From the results, NASNet was reported most efficient in classifying the blockage images with the 5-fold accuracy of 85% however, MobileNet was recommended for the hardware implementation because of its improved response time with 5-fold accuracy comparable to NASNet (i.e., 78%). Comparable performance to standard CNN models was achieved for the case where deep visual features were classified using conventional machine learning approaches. False negative (FN) instances, false positive (FP) instances and CNN layers activation suggested that background noise and oversimplified labelling criteria were two contributing factors in the degraded performance of existing CNN algorithms. A framework for partial automation of the visual blockage classification process was proposed, given that none of the existing models was able to achieve high enough accuracy to completely automate the manual process. In addition, a detection-classification pipeline with higher blockage classification accuracy (i.e., 94%) has been proposed as a potential future direction for practical implementation.

A Clinician-Led, Experience-Based Co-Design Approach for Developing mHealth Services to Support the Patient Self-management of Chronic Conditions: Development Study and Design Case

Publisher: JMIR Publications Inc.

Date: 20-07-2021

DOI: 10.2196/20650

Abstract: Despite the increasing use of mobile health (mHealth) services, such as mHealth apps or SMS text messaging services, that support the patient self-management of chronic conditions, many existing mHealth services lack theoretical guidance. In addition, although often the target audience for requirement acquisition at the initial mHealth app design stage, it is a common challenge for them to fully conceptualize their needs for mHealth services that help self-manage chronic conditions. This study proposes a novel co-design approach with the initial requirements for mHealth services proposed by clinicians based on their experiences in guiding patients to self-manage chronic conditions. A design case is presented to illustrate our innovative approach to designing an mHealth app that supports the self-management of patients with obesity in their preparation for elective surgery. We adopted a clinician-led co-design approach. The co-design approach consisted of the following four cyclic phases: understanding user needs, identifying an applicable underlying theory, integrating the theory into the prototype design, and evaluating and refining the prototype mHealth services with patients. Expert panel discussions, a literature review, intervention mapping, and patient focus group discussions were conducted in these four phases. In stage 1, the expert panel proposed the following three common user needs: motivational, educational, and supportive needs. In stage 2, the team selected the Social Cognitive Theory to guide the app design. In stage 3, the team designed and developed the key functions of the mHealth app, including automatic push notifications web-based resources goal setting and monitoring and interactive health-related exchanges that encourage physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. Push notifications were designed in response to a patient’s risk level, as informed by the person’s response to a baseline health survey. In stage 4, the prototype mHealth app was used to capture further requirements from patients in the two focus group discussions. Focus group participants affirmed the potential benefits of the app and suggested more requirements for the function, presentation, and personalization needs. The app was improved based on these suggestions. This study reports an innovative co-design approach that was used to leverage the clinical experiences of clinicians to produce the initial prototype app and the approach taken to allow patients to effectively voice their needs and expectations for the mHealth app in a focus group discussion. This approach can be generalized to the design of any mHealth service that aims to support the patient self-management of chronic conditions.

High frequency data in Land use and transport integrated model: A review of sources and application

Publisher: Elsevier BV

Date: 2023

DOI: 10.1016/J.EASTSJ.2022.100092

Transdisciplinary agricultural research in Lao PDR

Publisher: Elsevier BV

Date: 12-2019

DOI: 10.1016/J.JRURSTUD.2019.10.020

Prediction of Hydraulic Blockage at Culverts using Lab Scale Simulated Hydraulic Data

Publisher: Informa UK Limited

Date: 29-05-2022

DOI: 10.1080/1573062X.2022.2075770

Integrated optimisation of electric bus scheduling and top-up charging at bus stops with fast chargers

Publisher: IEEE

Date: 19-09-2021

DOI: 10.1109/ITSC48978.2021.9564617

A Game-Based Approach to Exploring Gender Differences in Smallholder Decisions to Change Farming Practices: White Rice Production in Laos

Publisher: MDPI AG

Date: 14-08-2020

DOI: 10.3390/SU12166594

Abstract: What influences farmers’ decisions to adopt agricultural technologies is an important question for international agricultural research projects. There are often interpersonal differences between women and men that influence the adoption of decisions and behaviours, but few studies in the literature focus on these factors. We describe a game-based approach to explore decision-making processes underpinning the adoption of new farming technologies and practices in Lao People’s Democratic Republic. Sowing a different rice variety is the tailored technology. The game explored adoption behaviours influencing decisions on transitioning from growing glutinous rice, a traditional variety preferred for consumption, to “white” rice for commercial export to international markets. We conducted separate game-workshops with 36 women and 36 men in 4 villages of southern Laos that were transitioning from subsistence to commercial smallholder production. The gaming exposed various possible behaviours and decisions that women and men considered. Access to resources, both assets and information, was equal for all players, yet women were found to adopt new rice varieties more readily than men and to engage in cooperative behaviours in the game situation. The study highlighted the need for further gender-sensitive research into cooperation among women in the agricultural context—an understanding beneficial for countries and regions undergoing agricultural transition.

Where is my Deer?-Wildlife Tracking And Counting via Edge Computing And Deep Learning

Publisher: IEEE

Date: 25-10-2020

DOI: 10.1109/SENSORS47125.2020.9278802

The price of a view: Estimating the impact of view on house prices

Publisher: Informa UK Limited

Date: 04-05-2019

DOI: 10.1080/14445921.2019.1626543

Collaborative translation of emergency messages (Co-TEM): An Australian case study

Publisher: Elsevier BV

Date: 11-2020

DOI: 10.1016/J.IJDRR.2020.101920

Exploring the impact of public transport including free and subsidised on the physical, mental and social well-being of older adults: a literature review

Publisher: Informa UK Limited

Date: 13-01-2021

DOI: 10.1080/01441647.2021.1872731

A Case Study of WiFi Sniffing Performance Evaluation

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/ACCESS.2020.3008533

Towards Agent-Based Traffic Simulation Using Live Data from Sensors for Smart Cities

Publisher: Springer International Publishing

Date: 2021

DOI: 10.1007/978-3-030-66888-4_3

A review of strategies for mitigating roadside air pollution in urban street canyons

Publisher: Elsevier BV

Date: 07-2021

DOI: 10.1016/J.ENVPOL.2021.116971

Problem-Driven and Technology-Enabled Solutions for Safer Communities

Publisher: Springer International Publishing

Date: 17-12-2021

DOI: 10.1007/978-3-030-15145-4_68-1

Employing the model based systems engineering methodologies to develop a domain specific language for contracting of infrastructure projects

Publisher: IEEE

Date: 04-2018

DOI: 10.1109/SYSCON.2018.8369559

A Clinician-Led, Experience-Based Co-Design Approach for Developing mHealth Services to Support the Patient Self-management of Chronic Conditions: Development Study and Design Case (Preprint)

Publisher: JMIR Publications Inc.

Date: 27-05-2020

DOI: 10.2196/PREPRINTS.20650

Abstract: espite the increasing use of mobile health (mHealth) services, such as mHealth apps or SMS text messaging services, that support the patient self-management of chronic conditions, many existing mHealth services lack theoretical guidance. In addition, although often the target audience for requirement acquisition at the initial mHealth app design stage, it is a common challenge for them to fully conceptualize their needs for mHealth services that help self-manage chronic conditions. his study proposes a novel co-design approach with the initial requirements for mHealth services proposed by clinicians based on their experiences in guiding patients to self-manage chronic conditions. A design case is presented to illustrate our innovative approach to designing an mHealth app that supports the self-management of patients with obesity in their preparation for elective surgery. e adopted a clinician-led co-design approach. The co-design approach consisted of the following four cyclic phases: understanding user needs, identifying an applicable underlying theory, integrating the theory into the prototype design, and evaluating and refining the prototype mHealth services with patients. Expert panel discussions, a literature review, intervention mapping, and patient focus group discussions were conducted in these four phases. n stage 1, the expert panel proposed the following three common user needs: motivational, educational, and supportive needs. In stage 2, the team selected the Social Cognitive Theory to guide the app design. In stage 3, the team designed and developed the key functions of the mHealth app, including automatic push notifications web-based resources goal setting and monitoring and interactive health-related exchanges that encourage physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. Push notifications were designed in response to a patient’s risk level, as informed by the person’s response to a baseline health survey. In stage 4, the prototype mHealth app was used to capture further requirements from patients in the two focus group discussions. Focus group participants affirmed the potential benefits of the app and suggested more requirements for the function, presentation, and personalization needs. The app was improved based on these suggestions. his study reports an innovative co-design approach that was used to leverage the clinical experiences of clinicians to produce the initial prototype app and the approach taken to allow patients to effectively voice their needs and expectations for the mHealth app in a focus group discussion. This approach can be generalized to the design of any mHealth service that aims to support the patient self-management of chronic conditions.

Edge-computing video analytics for real-time traffic monitoring in a smart city

Publisher: MDPI AG

Date: 02-05-2019

DOI: 10.3390/S19092048

Abstract: The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens’ privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.

Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors

Publisher: Public Library of Science (PLoS)

Date: 24-04-2020

DOI: 10.1371/JOURNAL.PONE.0231778

Crowdsourced social media data for disaster management: Lessons from the PetaJakarta.org project

Publisher: Elsevier BV

Date: 2019

DOI: 10.1016/J.COMPENVURBSYS.2018.09.002

An Ontology Based Context-Aware Architecture for Smart Campus Applications

Publisher: IEEE

Date: 12-2018

DOI: 10.1109/BDCLOUD.2018.00158

Application of Design Science Research to Design a Modelling Approach for Procurement of Infrastructure Systems

Publisher: IEEE

Date: 04-2019

DOI: 10.1109/SYSCON.2019.8836769

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Publisher: MDPI AG

Date: 16-11-2019

DOI: 10.3390/S19225012

Abstract: Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis

Publisher: MDPI AG

Date: 14-10-2022

DOI: 10.3390/S22207821

Abstract: The increased global waste generation rates over the last few decades have made the waste management task a significant problem. One of the potential approaches adopted globally is to recycle a significant portion of generated waste. However, the contamination of recyclable waste has been a major problem in this context and causes almost 75% of recyclable waste to be unusable. For sustainable development, efficient management and recycling of waste are of huge importance. To reduce the waste contamination rates, conventionally, a manual bin-tagging approach is adopted however, this is inefficient and requires huge labor effort. Within household waste contamination, plastic bags have been found to be one of the main contaminants. Towards automating the process of plastic-bag contamination detection, this paper proposes an edge-computing video analytics solution using the latest Artificial Intelligence (AI), Artificial Intelligence of Things (AIoT) and computer vision technologies. The proposed system is based on the idea of capturing video of waste from the truck hopper, processing it using edge-computing hardware to detect plastic-bag contamination and storing the contamination-related information for further analysis. Faster R-CNN and You Only Look Once version 4 (YOLOv4) deep learning model variants are trained using the Remondis Contamination Dataset (RCD) developed from Remondis manual tagging historical records. The overall system was evaluated in terms of software and hardware performance using standard evaluation measures (i.e., training performance, testing performance, Frames Per Second (FPS), system usage, power consumption). From the detailed analysis, YOLOv4 with CSPDarkNet_tiny was identified as a suitable candidate with a Mean Average Precision (mAP) of 63% and FPS of 24.8 with NVIDIA Jetson TX2 hardware. The data collected from the deployment of edge-computing hardware on waste collection trucks was used to retrain the models and improved performance in terms of mAP, False Positives (FPs), False Negatives (FNs) and True Positives (TPs) was achieved for the retrained YOLOv4 with CSPDarkNet_tiny backbone model. A detailed cost analysis of the proposed system is also provided for stakeholders and policy makers.

Correction to: An Architecture Framework Approach for Complex Transport Projects

Publisher: Springer Science and Business Media LLC

Date: 15-01-2020

DOI: 10.1007/S10796-020-09983-6

University Of Wollongong

Location: Australia

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Université Montpellier 2

Location: France

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University Of Melbourne

Location: Australia

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ARC Research Hub For Microrecycling Of Battery And Consumer Wastes

Start Date: 2019

End Date: 2023

Funder: Australian Research Council

Enhanced Modelling Capacity For The Industrial Ecology Virtual Laboratory

Start Date: 2016

End Date: 2016

Funder: Australian Research Council

ARC Training Centre For Advanced Technologies In Rail Track Infrastructure

Start Date: 2017

End Date: 2020

Funder: Australian Research Council

Urban Analytics Data Infrastructure

Start Date: 2016

End Date: 2016

Funder: Australian Research Council

Analytics To Predict Anaerobic Codigestion And Downstream Process Performance

Start Date: 2015

End Date: 2017

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100174

Start Date: 04-2016

End Date: 08-2017

Amount: $805,000.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0214943

Start Date: 2002

End Date: 12-2004

Amount: $73,715.00

Funder: Australian Research Council

Industrial Transformation Training Centres - Grant ID: IC170100006

Start Date: 12-2017

End Date: 12-2022

Amount: $3,937,625.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP110101720

Start Date: 10-2011

End Date: 12-2015

Amount: $438,774.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP150100304

Start Date: 06-2016

End Date: 12-2020

Amount: $470,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE220100025

Start Date: 11-2022

End Date: 06-2024

Amount: $548,940.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100066

Start Date: 04-2016

End Date: 06-2018

Amount: $260,000.00

Funder: Australian Research Council

Industrial Transformation Research Hubs - Grant ID: IH190100009

Start Date: 01-2021

End Date: 01-2026

Amount: $3,317,500.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR200100005

Start Date: 06-2021

End Date: 06-2030

Amount: $36,000,000.00

Funder: Australian Research Council