YOLOV8-FD: an improved lightweight algorithm for refrigerator door surface damage detection based on YOLOV8n

Abstract

To address the challenges of weak feature representation, background interference, and missed small-target detections in identifying subtle surface damages on highly reflective refrigerator doors during production, this paper proposes a lightweight detection algorithm, YOLOv8-FD, based on the YOLOv8n architecture. First, a lightweight GhostConv module replaces standard convolution layers within the backbone network. This is integrated with a modified lightweight CBAM attention mechanism (L-CBAM) to form the C2f-GC module, significantly enhancing fine-grained feature extraction under complex backgrounds while minimising parametric overhead. Furthermore, the SPPELAN module is incorporated to expand the receptive field and aggregate multi-scale features without compromising the lightweight design. To adapt to the geometric heterogeneity of defects, a novel Static-Asymmetry IoU (SAIoU) loss function based on a subtraction penalty mechanism is proposed, which optimises bounding box regression for irregular and asymmetric targets. Experimental evaluations on a strictly partitioned, independent hold-out test set demonstrate that YOLOv8-FD operates with only 5.3 M parameters and 6.0 GFLOPs of computational complexity, achieving reductions of 15.9% and 26.8%, respectively, compared to the baseline YOLOv8n model. Concurrently, it improves the mean average precision (mAP50) by 1.33 percentage points and the F1-score by 1.10 percentage points. These quantitative results empirically validate the efficacy of YOLOv8-FD in resolving fine-grained damage detection bottlenecks under resource-constrained edge-device scenarios.