A Lightweight YOLOv8 Tunnel Traffic Object Detection Method Based on Feature Enhancement and Adaptive Pruning

Tunnel traffic surveillance is challenged by uneven illumination, severe occlusion, distant small objects, and long-tailed class distributions, while practical edge deployment imposes strict computational constraints. To address these problems, this paper proposes an integrated object detection, structured compression, and edge-deployment framework based on YOLOv8. A C2f-P3A module is developed to enhance spatial and channel feature representation, while Lite-ASPP is introduced to efficiently incorporate multi-scale contextual information. WIoU v3 is adopted to optimize bounding-box regression, and a dependency-preserving channel-level LAMP strategy is employed to adaptively allocate sparsity across eligible layers. Experimental results on the tunnel-surveillance dataset show that the improved dense model achieves a Precision of 90.49%, a Recall of 75.91%, an mAP50 of 83.13%, and an mAP50:95 of 57.60%, outperforming YOLOv8s by 5.46, 6.56, 4.98, and 2.17 percentage points, respectively. Furthermore, a target channel sparsity of 60% reduces the parameter count from 11.24 M to 9.86 M and the computational cost from 28.4 to 25.7 GFLOPs, while retaining an mAP50 of 83.11%. After mixed-precision deployment on the RK3588 platform, the pruned model achieves an mAP50 of 72.87%, an average processing time of 39.04 ms/frame, and a processing rate of 25.61 FPS. These results indicate a favorable empirical trade-off among detection accuracy, model complexity, and edge-processing efficiency under the evaluated conditions.