A lightweight vehicles detection network model based on YOLOv5. (August 2022)

• Record Type: Journal Article
• Authors: Dong, Xudong; Yan, Shuai; Duan, Chaoqun
• Is Part Of: Engineering applications of artificial intelligence. Volume 113(2022)
• Publication Date: 2022-08
• Subjects: Vehicle detection -- Deep learning -- Attention mechanism -- GhostBottleneck -- Loss function; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104914
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Vehicle detection technology is of great significance for realizing automatic monitoring and AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of YOLOv5, has often been used to detect vehicles. However, it suffers some challenges, such as a high computational load and undesirable detection rate. To address these issues, an improved lightweight YOLOv5 method is proposed for vehicle detection in this paper. In the presented method, C3Ghost and Ghost modules are introduced into the YOLOv5 neck network to reduce the floating-point operations (FLOPs) in the feature channel fusion process and enhance the feature expression performance. A convolutional block attention module (CBAM) is introduced to the YOLOv5 backbone network to select the information critical to the vehicle detection task and suppress uncritical information, thus improving the detection accuracy of the algorithm. Furthermore, CIoU_Loss is considered the bounding box regression loss function to accelerate the bounding box regression rate and improve the localization accuracy of the algorithm. To verify the performance of the proposed approach, we tested our model via two case studies, i.e., the PASCAL VOC dataset and MS COCO dataset. The results show that the detection precision of the proposed model increased 3.2%, the FLOPs decreased 15.24%, and the number of model parameters decreased 19.37% compared with those of the existing YOLOv5. Through case studies and comparisons, the effectiveness and superiority of the presented approach are demonstrated. Highlights: Novel YOLOv5 with a lightweight design for vehicle detection. Convolutional block attention module improves model performance to select critical information. CIoU_Loss function to speed up bounding box regression rate and improve localization accuracy. Considerably better vehicle detection compared with related methods. Demonstration of the improved performance using the two real cases.