MoL-YOLOv7: Streamlining Industrial Defect Detection With an Optimized YOLOv7 Approach

Manually checking for defects on industrial parts such as steel surfaces is ineffective, error-prone, and can damage a company’s reputation. Current automated methods often lack accuracy or real-time detection capabilities. Early detection allows for timely corrective action, such as removing defective parts or adjusting production parameters which can streamline the manufacturing process and improve brand reputation, customer satisfaction, and compliance with industry standards. This paper presents MoL-YOLOv7 (MobileNet integrated with attention and loss function to You Only Look Once version 7), a deep learning model for accurate and real-time detection of steel defects. MoL-YOLOv7 modifies the YOLOv7 model by inserting a MobileNet block that reduces computational complexity while maintaining accuracy, allowing for faster detection. Adding the SimAm (Simple Parameter free Attention module) attention mechanism to the MobileNet block refines feature representations for complex tasks such as steel defect detection. Finally, replacing loss functions with EIoU (Effective IoU), WIoU (Wise-IoU), and SIoU (Scylla-IoU) improves the localization accuracy and addresses the class imbalance in the data. The modified model achieves high accuracy and real-time detection, enabling a streamlined defect detection process. Experimental results show that the modified model involving different loss functions used in this work achieves high accuracy, i.e. 0.5% to 3.5% higher than the original model YOLOv7. The superiority and validity of our modified model are demonstrated by comparison with other attention mechanisms and loss functions integrated into YOLOv7, and also on different texture datasets, putting forward a modified method to detect surface defects on steel strips in daily operations.