Abstract
In recent years, the YOLOv5 network architecture has demonstrated excellence in real-time object detection. For the purpose of applying in the smart parking management system, this paper proposes a network based on the improved YOLOv5, named YOLO5PKLot. This network focus on redesigning the backbone network with a combination of the lightweight Ghost Bottleneck and Spatial Pyramid Pooling architectures. In addition, this work also resizes the anchors and adds a detection head to optimize parking detection. The proposed network is trained and evaluated on the Parking Lot dataset. As a result, YOLO5PKLot achieved 99.6% mAP on the valuation set with only fewer network parameters and computational complexity than others.
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Acknowledgement
This result was supported by “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-003).
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Nguyen, DL., Vo, XT., Priadana, A., Jo, KH. (2023). YOLO5PKLot: A Parking Lot Detection Network Based on Improved YOLOv5 for Smart Parking Management System. In: Na, I., Irie, G. (eds) Frontiers of Computer Vision. IW-FCV 2023. Communications in Computer and Information Science, vol 1857. Springer, Singapore. https://doi.org/10.1007/978-981-99-4914-4_8
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DOI: https://doi.org/10.1007/978-981-99-4914-4_8
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