| Popis: |
To solve the problems of low accuracy, slow detection speed, and difficulty in deploying model parameters in surface defect detection of continuous casting production process, a lightweight surface defect detection algorithm YOLOv7-TSCR that integrates heavy parameterization and attention mechanism was proposed. Firstly, based on the Mish and SiLU activation functions and the SimAM attention mechanism, an improved high-efficiency layer aggregation module ELAN-S was constructed to effectively enhance the extraction of multi-scale defect features. Secondly, the C2f_RG module was designed to improve the feature fusion network, reducing the number of parameters while obtaining richer gradient flow information and enhancing feature fusion capabilities. Finally, based on the collected defect images from actual production, a dataset of casting defects was constructed and validated. The results show that YOLOv7-TSCR has significantly improved detection performance compared to other network models；With a reduced number of model parameters, the accuracy reaches 93.5%, the average accuracy increasesby 2.8%, and the detection speed reaches 120 FPS; The generalization comparison experiment on the NEU-DET public dataset proves that the algorithm has strong generalization. On the basis of ensuring high detection accuracy, the improved algorithm has a fast detection speed and a small number of parameters, which provides a technical reference for the efficient detection of surface defects in casting billets. |
