Car Detector Based on YOLOv5 for Parking Management

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.