A multiobject detection algorithm in complex traffic environments

The object detection algorithm named as ASF-YOLOv8 is proposed in this paper ,which is based on improved YOLOv8. Firstly, on the infrastructure of YOLOv8, the Attentional Scale Sequence Fusion-ASF is added to integrate feature maps at different scales and capture the image features of different scales, which can extract richer and more accurate feature information. Secondly, the channel and attention mechanism (CPAM) are added to improve the detection capability of targets of different scales. Finally, the loss function is improved by introducing Inner-IoU, which can further improve the detection accuracy of the algorithm by calculating the loss through auxiliary frame. The experimental results show that the detection accuracy mAP50 is improved by 1.5% on VisDrone dataset, so the proposed algorithm has more sufficient detection accuracy in complex traffic environment.