Fully Automatic and Precisely Woven Fabric Defect Detection Using Improved YOLOv7-Tiny Model Utilizing Enhanced Residual Convolutional Network

The field of fabric defect detection has undergone a transformative journey marked by the evolution of object detection models. From traditional approaches to advanced deep learning architectures, these models have addressed crucial challenges in the textile industry. YOLOv7-tiny model stands out as a remarkable solution, demonstrating unprecedented performance in fabric defect detection. Its enhanced architecture addresses key industry issues, including high-resolution images, small defect sizes, and imbalanced datasets. Therefore, the aim of this paper is to incorporate the YOLOv7 model with improvements to detect woven fabric defects in real time. Augmenting the Enhanced Residual Convolutional Network (ERCN) with extra Convolutional, batch normalization and leaky rectified linear unit (CBL) layers enhances hierarchical feature extraction, while the two-concatenation technique adds complexity for richer representations. Reducing CBL layers in Efficient layer aggregation networks-downgrade (ELAN-D) streamlines and optimizes, emphasizing a balanced approach in the YOLOv7-tiny model for targeted objectives. The improved YOLOv7-tiny model excels in achieving a delicate balance between accuracy and efficiency, vital for practical applications in the textile sector. This model's accuracy, with a mAP of 84% at a 0.50 threshold and 40% at 0.50:0.95 showed exceptional in comparisons to other models. The model also boasts a high accuracy of 98% and operates at a commendable detection speed of 90 fps, meeting real-time demands in fabric production. Recognizing defects as small as 1 mm, the YOLOv7-tiny model emerges as a pivotal tool in automating fabric defect detection and optimizing textile quality management processes.