Vehicle detection algorithm for highway driving scenarios

Detecting vehicles is a crucial task in autonomous driving systems for highways. However, current detection algorithms used in such scenarios may face issues such as overlooking or misidentifying small or faraway vehicles, as well as overall low detection accuracy in different situations. To tackle these challenges, this paper proposes a vehicle detection algorithm called CC-YOLO that specifically targets the highway driving scene. CC-YOLO is an improved version of YOLOv5s, leveraging the C2F module from YOLOv8 to replace the original C3 module in YOLOv5s’ Backbone and PAN-FPN. This replacement enhances the detection accuracy of vehicles while maintaining a lightweight model. In addition, the PAN-FPN is upgraded with a lightweight upsampling operator known as CARAFE to improve the recognition rate of small target vehicles. Finally, the detection head is enhanced with an ECA attention module to better learn vehicle features and improve the accuracy of vehicle detection. A vehicle detection dataset is constructed using real highway driving videos, and CC-YOLO is evaluated on this dataset. The experimental results indicate that, compared to YOLOv5s, CC-YOLO achieves an increased mAP of 6.8%, effectively improving vehicle detection accuracy in the highway driving scene.