YOLOv8-DSM: High-Precision Small Target Traffic Detection Algorithm

To address the issues of low accuracy, false positives, and false negatives in the detection of distant, small, and occluded objects, we proposes an improved algorithm named YOLOv8-DSM (deep small-object model), based on YOLOv8, as a replacement for the original framework. The improvements include: replacing the original framework with DWConv (depth-wise separable convolution) and GELU (Gaussian error linear unit) activation functions to reduce computational complexity and create a lightweight network; designing an SPPFCG (SPPF-Conv-AvgPool) module to replace the SPPF (spatial pyramid pooling - fast) module, minimizing local feature loss while mitigating overfitting and enhancing feature diversity; introducing the DPC2f (dual-pooling C2f) module as a substitute for C2f module to strengthen multiscale feature extraction and improve recognition of varying-sized targets; incorporating a small-target detection head and an SA (shuffle attention) mechanism to boost sensitivity to small-target detection features; proposing the MPDIoU_P (maximum precision distance IoU_ precise) loss function with enhanced penalty terms for width and height errors to refine small-target localization. The optimized network demonstrates superior accuracy in detecting small and obscured objects, accelerated convergence, and improved average precision. Evaluations on the KITTI dataset reveal that YOLOv8-DSM surpass YOLOv8n by4.6% higher mAP (mean average precision), 7.1% higher recall rate, and 5.9×10⁶ fewer parameters. Module comparison and ablation experiments validate the algorithm’s practicality and advantages for small-target and occluded-object detection tasks. Generalization experiments conducted on the VisDrone dataset further validated the effectiveness and generalization ability of our improved model for object detection tasks in traffic scenarios.