Addressing various types of defects commonly found in the cotton fabric production process,and considering the significant differences in scale,diverse shapes,low contrast,and small object defects,an improved object detection model,DCA-CenterNet,based on the CenterNet network is proposed.This model is applied to detect defects in cotton fabric for the first time.By introducing the Coordinate Attention into the residual module of the backbone network Hourglass,the model can capture spatial relationships and contextual information of different positions,enhancing the feature expression capability for cotton fabric defects.Key point filtering module is designed to effectively capture crucial position information,thereby improving the algorithm model's detection accuracy.Multiple groups of improved detectors with key point filtering modules based on localization quality are employed to better adapt to the diversity and scale differences of cotton fabric defects,effectively addressing the issue of extreme aspect ratios in cotton fabric defects.Experimental results demonstrate that the proposed model achieves a 4.14%improvement in mAP compared to the original model.Furthermore,it outperforms YOLOv5 and Faster RCNN algorithms by 4.20%and 9.11%,respectively.These validate the effectiveness of the proposed model.
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