Design of Non-Woven Needle Defect Detection and Replacement Equipment
This article addresses the issue of manual dependence in detecting needle defects in conventional non-woven fabric manufacturing.To tackle this problem,a real-time needle defect detection and replacement system based on the YOLOV5 algorithm is proposed.The hardware system comprises modules for image capture,processing,needle retraction,and needle replacement,utilizing deep learning to enhance detection efficiency and accuracy while meeting stability,real-time,and scalability requirements.The software interface design incorporates data visualization for effective management and analysis of detection data.Overall,the proposed intelligent device introduces advanced technology,boosting efficiency,reducing costs,and creating comprehensive value for the sustainable development of the non-woven fabric industry.
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