FlexiNet: Fast and Accurate Vehicle Detection for Autonomous Vehicles

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aut.relation.conferenceICCCV'21: 2021 4th International Conference on Control and Computer Visionen_NZ
aut.researcherYan, Wei Qi
dc.contributor.authorMehtab, Sen_NZ
dc.contributor.authorSarwar, Fen_NZ
dc.contributor.authorYan, Wen_NZ
dc.date.accessioned2021-11-28T23:35:22Z
dc.date.available2021-11-28T23:35:22Z
dc.date.copyright2021-08-13en_NZ
dc.date.issued2021-08-13en_NZ
dc.description.abstractAutonomous vehicle has come to reach on the road; however accurate road perception in real-time is one of the crucial factors towards its success. The greatest challenge in this direction includes occlusion, truncation, lighting conditions, and complex backgrounds. In order to improve the accuracy and detection speed of vehicle detection, a dynamic scaling network is proposed that assists in constructing a balanced shape neural network to achieve optimum accuracy with minimal hardware. The net architecture is influenced by YOLOv5 and is composed of Cross-Stage Partial Network (CSPNet) as its backbone. In order to go even further, we have proposed an auto-anchor generating method that makes the network suitable for any datasets. Our neural network is fine-tuned by using activation, loss, and optimization functions so as to get the optimum results. Our experimental results demonstrate that the proposed net provides comparable performance of YOLOv4 and Faster R-CNN based on KITTI dataset as the benchmark.
dc.identifier.citationIn 2021 4th International Conference on Control and Computer Vision (ICCCV'21). Association for Computing Machinery, New York, NY, USA, 43–49. DOI:https://doi.org/10.1145/3484274.3484282
dc.identifier.doi10.1145/3484274.3484282en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14742
dc.publisherACMen_NZ
dc.relation.urihttps://dl.acm.org/doi/10.1145/3484274.3484282
dc.rights© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION (see Citation), (see Publisher’s Version).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectAutonomous driving; Self-driving car; Deep Neural Network; YOLOv4; YOLOv5
dc.titleFlexiNet: Fast and Accurate Vehicle Detection for Autonomous Vehiclesen_NZ
dc.typeConference Contribution
pubs.elements-id444654
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Centre for Robotics & Vision
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Science, Technology, Engineering, & Mathematics Tertiary Education Centre
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/AP - Design and Creative Technologies
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