Jinhai Wang1, Wei Wang1, Zongyin Zhang1, Xuemin Lin1, Jingxian Zhao1, Mingyou Chen1, Lufeng Luo2,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 759-780, 2024, DOI:10.32604/cmc.2023.041600
Abstract As computer technology continues to advance, factories have increasingly higher demands for detecting defects. However, detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes. To address this issue, this paper proposes YOLO-DD, a defect detection model based on YOLOv5 that is effective and robust. To improve the feature extraction process and better capture global information, the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer (RDAT). Additionally, an Information Gap Filling Strategy (IGFS) is proposed to improve the fusion of features at different… More >
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