Multiclass Classification and Defect Detection of Steel tube using modified YOLO

Saraee, Mo; khan, Surbhi

Abstract

Steel tubes are widely used in hazardous high pressure environments such as petroleum, chemicals, natural gas and shale gas. Defects in steel tubes have serious negative consequences. Using deep learning object recognition to identify and detect defects can greatly improve inspection efficiency and drive industrial automation. In this work, we use a well-known YOLOv7(You Only Look Once version7) deep learning model and propose to improve it to achieve accurate defects detection of steel tube images. First, the classification of the dataset is checked using a sequential model and AlexNet. A Coordinate Attention (CA) mechanism is then integrated into the YOLOv7 backbone network to improve the expressive power of the feature graph. Additionally, the SIoU (SCYLLAIntersection over Union) loss function is used to speed up convergence due to class imbalance in the dataset. Experimental results show that the evaluation index of the optimized and modified YOLOv7 algorithm outperforms other models. This study demonstrates the effectiveness of using this method in improving the model’s detection performance and providing a more effective solution to steel tube defects.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 International Conference on Neural Information Processing (ICONIP2023),
• Start Date: Nov 20, 2023
• End Date: Nov 23, 2023
• Acceptance Date: Aug 31, 2023
• Online Publication Date: Nov 20, 2023
• Publication Date: Nov 20, 2023
• Deposit Date: Nov 18, 2023
• Publisher URL: http://iconip2023.org/web_program.htm