VEHICLE TARGET DETECTION ALGORITHM BASED ON IMPROVED YOLOV5 LIGHTWEIGHT

Driverless cars have made tremendous progress and breakthroughs in recent years. As an important prerequisite for driverless cars to drive safely, environmental perception technology needs to detect their surroundings in advance during driving, and quickly and accurately detect the surroundings target. Based on this problem, this paper proposes a target detection algorithm based on improved YOLOv5. EfficientNetV2 was used as the backbone feature extraction network of the YOLOv5 algorithm. In order to improve the convergence of the algorithm, the MetaAconC activation function was introduced, and BiFPN was integrated in the Head, which increased the diversity of image feature fusion, reduced the algorithm model by 39%, and there was also a certain improvement in accuracy. Through experimental verification, compared with the original method of YOLOv5, this algorithm has higher detection accuracy while ensuring real-time target detection, and has better equipment compatibility.