Car Detection for Smart Parking Systems Based on Improved YOLOv5

Abstract

Nowadays, YOLOv5 is one of the most popular object detection network architectures used in real-time and industrial systems. Traffic management and regulation are typical applications. To take advantage of the YOLOv5 network and 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. On the other hand, this work also presents an aerial view dataset for car detection tasks in the parking, named the AVPL. 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 one proposed dataset. The experiments are 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%, 97.4%, and 97.0% of mAP@0.5 on the Car Parking Lot Dataset, the Pontifical Catholic University of Parana+ Dataset, and the proposed AVPL dataset, respectively. A set of demonstration videos and the proposed dataset are available here: https://bit.ly/3YUoSwi .