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https://elib.vku.udn.vn/handle/123456789/2742
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, Duy Linh | - |
dc.contributor.author | Vo, Xuan Thuy | - |
dc.contributor.author | Adri, Priadana | - |
dc.contributor.author | Kang-Hyun, Jo | - |
dc.date.accessioned | 2023-09-26T02:15:56Z | - |
dc.date.available | 2023-09-26T02:15:56Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.isbn | 978-3-031-36886-8 | - |
dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-031-36886-8_9 | - |
dc.identifier.uri | http://elib.vku.udn.vn/handle/123456789/2742 | - |
dc.description | Lecture Notes in Networks and Systems (LNNS, volume 734); CITA: Conference on Information Technology and its Applications; pp: 102-113. | vi_VN |
dc.description.abstract | Nowadays, YOLOv5 is one of the most widely used object detection network architectures in real-time systems for traffic management and regulation. To develop a parking management tool, this paper proposes a car detection network based on redesigning the YOLOv5 network architecture. This research focuses on network parameter optimization using lightweight modules from EfficientNet and PP-LCNet architectures. The proposed network is trained and evaluated on two benchmark datasets which are the Car Parking Lot Dataset and the Pontifical Catholic University of Parana+ Dataset and reported on mAP@0.5 and mAP@0.5:0.95 measurement units. As a result, this network achieves the best performances at 95.8 % and 97.4 % of mAP@0.5 on the Car Parking Lot Dataset and the Pontifical Catholic University of Parana+ Dataset, respectively. | vi_VN |
dc.language.iso | en | vi_VN |
dc.publisher | Springer Nature | vi_VN |
dc.subject | Convolutional neural network (CNN) | vi_VN |
dc.subject | EfficientNet | vi_VN |
dc.subject | PP-LCNet | vi_VN |
dc.subject | Parking management | vi_VN |
dc.subject | YOLOv5 | vi_VN |
dc.title | Car Detector Based on YOLOv5 for Parking Management | vi_VN |
dc.type | Working Paper | vi_VN |
Appears in Collections: | CITA 2023 (International) |
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