In the age of smart technology,the widespread use of small LCD(Liquid Crystal Display)necessitates pre-market defect detection to ensure quality and reduce the incidence of defective products.Manual inspection is both...In the age of smart technology,the widespread use of small LCD(Liquid Crystal Display)necessitates pre-market defect detection to ensure quality and reduce the incidence of defective products.Manual inspection is both time-consuming and labor-intensive.Existing methods struggle with accurately detecting small targets,such as point defects,and handling defects with significant scale variations,such as line defects,especially in complex background conditions.To address these challenges,this paper presents the YOLO-DEI(Deep Enhancement Information)model,which integrates DCNv2(Deformable convolution)into the backbone network to enhance feature extraction under geometric transformations.The model also includes the CEG(Contextual Enhancement Group)module to optimize feature aggregation during extraction,improving performance without increasing computational load.Furthermore,our proposed IGF(Information Guide Fusion)module refines feature fusion in the neck network,preserving both spatial and channel information.Experimental results indicate that the YOLO-DEI model increases precision by 2.9%,recall by 13.3%,and mean Average Precision(mAP50)by 12.9%,all while maintaining comparable parameter counts and computational costs.These significant improvements in defect detection performance highlight the model’s potential for practical applications in ensuring the quality of LCD.展开更多
文摘In the age of smart technology,the widespread use of small LCD(Liquid Crystal Display)necessitates pre-market defect detection to ensure quality and reduce the incidence of defective products.Manual inspection is both time-consuming and labor-intensive.Existing methods struggle with accurately detecting small targets,such as point defects,and handling defects with significant scale variations,such as line defects,especially in complex background conditions.To address these challenges,this paper presents the YOLO-DEI(Deep Enhancement Information)model,which integrates DCNv2(Deformable convolution)into the backbone network to enhance feature extraction under geometric transformations.The model also includes the CEG(Contextual Enhancement Group)module to optimize feature aggregation during extraction,improving performance without increasing computational load.Furthermore,our proposed IGF(Information Guide Fusion)module refines feature fusion in the neck network,preserving both spatial and channel information.Experimental results indicate that the YOLO-DEI model increases precision by 2.9%,recall by 13.3%,and mean Average Precision(mAP50)by 12.9%,all while maintaining comparable parameter counts and computational costs.These significant improvements in defect detection performance highlight the model’s potential for practical applications in ensuring the quality of LCD.
