Vehicle target detection algorithm based on yolov5

Abstract

With the rapid advancement of automobile manufacturing technology and the intensification of competition among automobile brands, the development of autonomous driving has been pushed to the forefront of automobile development, which causes approximately $650 billion in losses due to traffic accidents worldwide every year. For in the complex vehicle target scene, because of its many vehicle targets, dense targets often exist between the occlusion, overlap, a variety of different weather reasons, rain, fog, sunny days, etc. resulting in obstruction of the field of view, and the vehicle camera often intercepted images mostly exist blurred, ghosting and other problems, making the vehicle target detection for detailed feature extraction requirements are high, the detection accuracy is often difficult to meet the requirements, proposed based on YOLOv5s vehicle target detection algorithm is proposed. ACmix attention mechanism is introduced to make the model achieve the purpose of expanding the target perception field, adaptively focusing on different target regions, and capturing more information features, etc. The test shows that the accuracy of the model increases by 1.1% on a subset of BDD100K data set after improvement. The accuracy of the improved model increased by 1.1% on the subset of BDD100K dataset and by 1.2% on the PASCAL VOC2007 general dataset.