An Improved YOLOv8n-Based Algorithm for Body-in-White Spot-Weld Defect Detection

Abstract

In automotive body-in-white (BIW) inspection, small spot-weld defects can be obscured by metal-surface reflections and by the similar morphology of normal and defective welds. This study proposes FasterNet-Context-Guided Block-C2f-CNCM YOLO (FCC-YOLO), an improved YOLOv8n detector that applies stage-wise feature compensation. A FasterNet Block enhances local defect details in the backbone, a Context-Guided Block improves defect–background discrimination in the neck, and C2f-CNCM combines spatial, column, and self-calibration branches during multiscale fusion. On a dataset of 820 images (656 training, 82 validation, and 82 test images), FCC-YOLO achieved mAP50 of 96.0% and mAP50-95 of 74.7%, improvements of 3.0 and 3.2 percentage points over YOLOv8n. On an NVIDIA Jetson AGX Orin with TensorRT FP16, it processed images at 56.26 FPS with 17.77 ms mean end-to-end latency. These results demonstrate a favorable accuracy–efficiency trade-off in the evaluated dataset and deployment environment. Generalization across production lines, materials, and illumination conditions remains to be validated.