Research on target detection based on improved YOLOv7 in complex traffic scenarios

Target detection is an essential direction in artificial intelligence development, and it is a crucial step in realizing environmental awareness for intelligent vehicles and advanced driver assistance systems. However, the current target detection algorithms applied to complex real-life scenarios still have a lot of intractable problems, such as the detection of different road scenarios, not having a good real-time detection capability, and so on. Therefore, there is a need to balance the efficiency and effectiveness of the target detector. YOLOv7, as a single-stage target detection algorithm, combines a number of advanced modules and methods for the purpose of achieving more precise and faster target detection. This paper, YOLOv7 is used as a baseline, combined with deformable convolution, to realize adequate detection in complex scenes and balanced computational efficiency and accuracy by adding an attention mechanism module. In addition, combining the lightweight network module speeds up the model's computational speed while improving the detector's feature expression ability, thus accomplishing the task of real-time target detection in complex traffic scenes. Compared with YOLOv7, our model improves the average accuracy by 3.7% on the SODA 10M dataset, and the mean average precision (mAP) value reaches 75.9%.