Improved SSD foreign fiber detection method based on convolutional neural network lightweighting

Accurate detection of small foreign fibers mixed in cotton is the basis and key to guarantee the quality of yarn and fabric. Aiming at the problems of high leakage rate and complex network structure of existing algorithms in the detection of small foreign fibers in cotton, an improved Single Shot multibox Detector (SSD) based on convolutional neural network lightweight was proposed for the detection of foreign fibers in cotton. The original backbone feature extraction network VGGNetlö in the SSD algorithm was replaced with MobileNetv2 network by introducing innovative designs such as depth-separable convolution and inverted residual structure; for the problem that the can-didate box size generated in SSD algorithm did not match the size of cotton foreign fibers leading to a high percentage of the cotton background, which caused the imbalance of the positive and negative samples, the K-means++ algorithm was used to determine the candidate box size of cotton foreign fibers and make Cluster analysis, thus the cotton foreign fiber size and the candidate frame size were corrected according to the clustering results. The results showed that the proposed method effectively improved the effect of foreign fiber detection and computational efficiency while realizing model lightweighting. © 2025 CIMS. All rights reserved.