液晶与显示2026，Vol.41Issue(2)：208-221,14.DOI:10.37188/CJLCD.2025-0220
FFD-YOLO:面向小目标与复杂背景的液晶屏缺陷检测
FFD-YOLO:LCD screen defect detection for small targets and complex backgrounds
摘要
Abstract
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.关键词
液晶屏/缺陷检测/特征金字塔共享卷积/多尺度自适应卷积/YOLOv8Key words
liquid crystal display/defect detection/feature pyramid shared convolutions/multi-scale adaptive convolutions/YOLOv8分类
信息技术与安全科学引用本文复制引用
山宏刚,倪奕麟,蔡建刚,朱军,廖泽威,戚顺楠..FFD-YOLO:面向小目标与复杂背景的液晶屏缺陷检测[J].液晶与显示,2026,41(2):208-221,14.基金项目
海关机电类实验室智慧检验检测场景的研究及实践项目(No.2024HK078)Supported by Project of Research and Practice of Intelligent Inspection and Testing Scenarios in Customs Electromechanical Laboratories(No.2024HK078) （No.2024HK078）
