Automated assessment of hydrostatic pressure resistance in woven fabrics using the enhanced YOLOv8 network

Hydrostatic pressure resistance serves as a crucial metric in the assessment of water resistance in woven fabrics. The expeditious, precise, and efficient conduct of hydrostatic pressure tests holds paramount importance in advancing the progress and production of high-performance textiles. Addressing the challenges posed by intricate printed patterns on woven fabrics, and the presence of small, widely scattered water droplets, the study leverages the enhanced YOLOv8 model to develop a machine vision-based automated detection technique for assessing water resistance in woven fabric. The proposed method incorporates convolutional block attention module attention mechanisms into the backbone and neck network, replaces the path aggregation network structure of YOLOv8 with the bidirectional feature pyramid network structure, and introduces a dedicated detection head for small targets. These enhancements facilitate accurate identification of water outlet points on the woven fabric and precise recording of frame positions, enabling the precise measurement of hydrostatic pressure. Validation of the proposed model is conducted through a series of comparative experiments utilizing a self-collected dataset. The experimental results underscore the exemplary performance of the proposed model, evidenced by an AP0.5 score of 92.18%, showcasing superior overall efficacy in comparison with alternative models. Notably, the target localization time error is found to be less than 2 s when contrasted with manual detection. This method substantially enhances the accuracy of water droplet detection and localization in hydrostatic pressure resistance testing of woven fabric, characterized by complex surface patterns, thereby contributing to refinement of hydrostatic pressure testing methodologies in woven fabric analysis.