Fast Textile Pilling Classification Based on a Lightweight Network and 3D Point Clouds

Yu Lu, Yizhou Jin, Yuyu Chen, Gang Zhou, Zhenghui Hu, Qingjie Liu, Di Huang, Yunhong Wang

Abstract: Point clouds have demonstrated extensive application prospects in various fields, including research related to the evaluation of textile pilling. We collect 3D point cloud data in the actual test environment of textiles, which has been organized and named the TextileNet dataset. To the best of our knowledge, it is the first publicly available 3D point cloud dataset in the field of textile pilling assessment. Based on the Non-parametric Network for 3D point cloud analysis (Point-NN), we construct a Few-parameter Network called Point-FN for experiments on the TextileNet dataset. Experimental results indicate that under conditions with a parameter count of only 0.5M and FLOPs of 1.7G, Point-FN achieves an Overall Accuracy (OA) of 91.1% and a Mean per-class Accuracy (MA) of 93.0%. Moreover, under the testing conditions of a single RTX 2080Ti GPU, Point-FN demonstrates an inference speed of 164 FPS. Testing results on other publicly available datasets also validate the competitive performance of Point-FN. The proposed TextileNet dataset will be publicly available.