Lightweight Vehicle Damage Detection Using GSConv-Based Slim-Neck and Bi-Level Routing Attention

Vehicle damage detection is an important task in intelligent transportation systems and insurance assessment, yet it remains challenging due to the subtle appearance, irregular shapes, and spatial dispersion of damage regions in complex environments. We propose a specialized structural synergy that organically integrates a GSConv-based Slim-Neck, a dynamic Bi-Level Routing Attention mechanism, and an orientation-aware SIoU loss. Rather than a superficial architectural combination, this cooperative design introduces a novel methodological framework engineered specifically to resolve the fundamental conflict between edge-deployment efficiency and fine-grained feature preservation in vehicle inspection. The method is evaluated on the publicly available Car Damage Detection dataset and compared with representative two-stage and one-stage detectors, including DETR, Faster R-CNN, YOLOv5n, YOLOv8n, and YOLO11n. Experimental results show that the proposed approach achieves a mAP50 of 67.9% and mAP50–95 of 53.8%, outperforming the baseline YOLO11n and other lightweight YOLO variants with only a moderate increase in computational cost. These results indicate that the proposed framework offers a favorable trade-off between detection accuracy and efficiency, showing potential for vehicle damage inspection under resource-constrained conditions.