Lightweight fabric defect detection algorithm for embedded device deployment.

In response the issues of large parameter quantity, high commitational complexity, and difficulty in deploying the existing defect detection algorithms on embedded devices with limited computing resources, an improved lightweight fabric defect detection algorithm SSPY based on YOLOv5s was proposed. Firstly, the ShuffleNetv2 network was used as the backbone feature extraction network to achieve lightweight model. The SimAM no-parameter attention mechanism was introduced in the backbon network and small target detection layer, enhancing the feature extraction capabilities of the algorithm without adding additional parameters. Model compression was further achieved by pruning based on the importance of the convolution kernel in the sparse training evaluation feature extraction layer. Finally, the SSPY algorithm was deployed on Rockchip RK3568 platform, and the deployment of the fabric defect real-time detection algorithm was completed on embedded devices. Multiple comparison experiments were carried out on fabric defect data set. Experimental results show that compared with Y0L0v5s, SSPY's mAP increases by 0.8% and the number of parameters decreases by 80. 3%. When SSPY was deployed on RK3568, the running speed can reach 49 FPS, which met the needs of real-time performance and embedded device deployment of fabric defect detection algorithms in industrial applications. [ABSTRACT FROM AUTHOR]