关键词: 交通场景, YOLOv8, C2Former, 多频单通道注意力, 频域信息

Abstract: Aiming at the low detection and positioning accuracy of small targets and occluded targets in the traffic scene, and the detection effect is subject to contrast changes and image noise, this paper proposes a vehicle target detection model YOLO-CDC based on YOLOv8. Firstly, this paper replaces the C2f module combining the global feature extraction capability of Transformer structure with C2Former module, and designs a gated repetitive multi-layer perception (GRMLP) unit to enhance Transformer’s branch nonlinearity expression capability and realize high-quality global information aggregation. Secondly, a multi-scale feature fusion branch is designed for feature integration: combining deformable convolution and SPDconv module to extract P2 layer resolution information and capture finer edge features, and the CSP_FRM (CSP feature reconstruct module) module is designed to balance the computational burden of large kernel convolution and frequency domain features. Finally, a multi-scale feature enhancement module is designed: using large kernel convolution and strip convolution to expand receptive fields and supplement context information, and using non-step convolution to supplement the fine-grained details of the target. Combined multi-frequency single-channel attention mechanism and channel attention, it designs an coupling spatial and frequency-domain attention to suppress image noise and background interference. The experimental results show that the average accuracy (mAP50-95) on the UA-DETRA dataset and SODA10M dataset is increased by 3.6 and 4.1 percentage points respectively, the detection speeds is 340.1 FPS and 341.4 FPS, respectively, demonstrating the higher detection and positioning accuracy and generalization of the improved algorithm.

Key words: traffic scene, YOLOv8, C2Former, multi-frequency single-channel attention, frequency-domain information