Surface defect detection method of disposable bamboo chopsticks based on multi-scale fusion
Surface defect detection and identification is an important task in the product quality control process.In view of the current problems of low intelligence and high false detection rate in disposable bamboo chopsticks production inspection,a cross-scale weighted feature for identifying defect categories and locating defects is proposed.Converged Network.Due to problems such as unclear defect edges,small size,and background texture interference,an improved Retinex image enhancement method is pro-posed.However,the data generation process is expensive and data with defective samples rarely appears.A large number of normal disposable bamboo chopsticks samples are used for feature extraction to improve the generalization ability of surface defect recogni-tion tasks,and the inverse residual architecture is combined with the Coordinate Attention mechanism(CA)are combined to en-hance the robustness of multi-size defect detection.Experimental results show that the proposed method can effectively improve the performance of disposable bamboo chopsticks detection and recognition,and the detection accuracy can reach 92.6%,meeting the needs of actual production.
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