Target detection in autonomous driving scenarios faces challenges such as complex environmental interference, multi-scale target distribution and target occlusion, and existing algorithms are still deficient in feature fusion capability, detail characterization accuracy and localization regression performance. To this end, an improved YOLOv8 detection algorithm, DMP-YOLO, is proposed. The original neck structure is optimized using Multi-Branch Auxiliary Feature Pyramid Network (MAFPN) to enhance the multi-scale feature fusion capability in complex traffic scenarios; C2f_DEConv is proposed in backbone network module, which replaces the standard convolution with Detail-Enhanced Convolution (DEConv) to significantly improve the detail capturing ability of small-scale vehicles and occluded targets through high-frequency feature preservation and local texture enhancement; the Powerful Intersection over Union version 2 (PIoUv2) loss function is introduced to optimize the improved bounding-box loss, which improves the regression accuracy of the target bounding-box through the optimization of dynamic scale-sensitive factors and geometric constraints. Experiments on the KITTI dataset demonstrate that DMP-YOLO achieves significant improvements across all key performance metrics, with mAP@0.5 reaching 89.0% (2.6% improvement compared with the baseline YOLOv8) as well as 2.9% improvement for mAP@0.5: 0.95, which provides an effective solution for high-precision real-time detection in autonomous driving scenarios.

autonomous driving  /  YOLOv8  /  MAFPN  /  PIoUv2 loss function