FFD-YOLO:面向小目标与复杂背景的液晶屏缺陷检测OA

Surface defects on liquid crystal displays(LCDs)impair appearance and reliability,presenting challenges such as wide-scale variations,complex backgrounds,and difficulty in detecting small targets.This paper proposes FFD-YOLO,an LCD defect detection algorithm based on the lightweight YOLOv8n framework.The algorithm uses the FasterNet backbone to enhance feature extraction.It designs the Feature Pyramid Shared Convolution(FPSC)module,which uses multi-expansion-rate convolutions and shared convolutional mechanisms to enhance multi-scale feature modeling.Additionally,it proposes the Multi-Scale Adaptive Convolution Module(MACM),which employs dynamic convolution weights and multi-scale convolution kernels to enhance the representation of small objects and stability in complex backgrounds.Experimental results demonstrate that on the self-built industrial-grade LCD-NET dataset,FFD-YOLO achieves 5.0%,4.7%,and 3.2%improvements in Precision,Recall,and mAP50,respectively,compared to baseline models,with a 6.4%boost in accuracy for detecting small stain-type objects.These results demonstrate that FFD-YOLO significantly enhances LCD defect detection performance while maintaining lightweight efficiency,offering an effective and reliable solution for industrial vision inspection systems.