Dynamic YOLOv8n: a vehicle detection network model based on YOLOv8n

For the vehicle edge computing platform, a huge model is difficult to achieve the requirements of real-time detection, which faces the challenges of high computational load and bad detection rate. In this paper, an improved YOLOv8n detection method is proposed, and a dynamic deformable convolution block (DDCNv2) is proposed in the YOLOv8n backbone network to improve the detection accuracy of the algorithm. A dynamic convolution module (KWConv) is introduced in the YOLOv8n. In addition, a multi-scale detector module is designed to reduce the number of parameters. We use the PASCAL VOC dataset and the MS COCO dataset. The results show that compared with the existing YOLOv8n, the detection accuracy of the proposed model is improved by 5.1%, the FLOPs is reduced by 69.51%, and the model parameters is reduced by 8.64%, which proves the effectiveness and superiority of the proposed method.