Improved Complex Road Scene Object Detection Algorithm of YOLOv7

Although the target detection algorithm based on deep learning has achieved good results in the target detection in the road scene, for the dense targets in the complex road scene, the detection accuracy of distant small-scale targets is low, and the problem of missing detection and false detection is easy to occur. An improved YOLOv7 target detection algorithm in the complex road scene is proposed. It adds small target detection layer, increases the feature learning ability of small target; K-means++ is used to reunite the prior frame, which makes the prior frame fit the target better and increases the positioning accuracy of the target. WIoU (Wise-IoU) loss function is used to increase the attention of the network to the common mass anchor frame and improve the ability of the network to locate the target. CoordConv is introduced into the neck and detection head, so that the network can better sense the position information in the feature map. P-ELAN structure is proposed to reduce the number of algorithm parameters and the amount of computation. The experimental results show that the mAP of the improved algorithm under Huawei SODA10M dataset reaches 64.8%, which is 2.6 percentage points higher than the original algorithm. The number of model parameters and the amount of computation are reduced by 12% and 7% respectively, to achieve the balance of detection accuracy and detection speed. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.