Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once
Year: 2024
Type: article
Abstract: In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects.
Source: Sensors
DOI: 10.3390/s24175636
Authors: Zhihao Jiang, Xiaohan Dou, Xiaolong Liu, Chengqi Xue, Anqi Wang, Gengpei Zhang
Institution: Yangtze University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4402052954