Vehicle Detector Based on Improved YOLOv5 Architecture for Traffic Management and Control Systems

Vehicle detection is an important module in traffic management and control systems. These systems require compactness, mobility, and high accuracy when deployed in a real-time context. Based on the YOLOv5 network architecture, this paper proposes several improvements to increase the performance and speed of the network when applied to vehicle detection. The research aims to redesign the backbone and neck modules with lightweight convolutional network architectures such as EfficientNet, PP-LCNet, and MobileNet. In addition, the Squeeze-and-Excitation (SE) attention architecture is also used inside the above-mentioned architectures to help the network focus on salient information during feature extraction. The network is trained and evaluated on a modified and normalized dataset of the UA-DETRAC dataset. As a result, the proposed network achieves 58.1% of mAP@0.5 and 40.1% of mAP@0.5:0.95 with just over ten million network parameters. This result outperforms other methods and is comparable to the lightweight architectures of the YOLOv5 family.