YOLOv5s fabric defect detection model with mixed attention mechanism


  • Zekai Kong




Fabric defects, Deep learning, Object detection, YOLOv5s, ACmix


In the field of industrial quality inspection, fabric defect detection is regarded as an important research topic. The traditional inspection method mainly relies on manual visual inspection, which is time-consuming and easy to be affected by human factors, and has the problems of low accuracy, slow speed and high cost. Therefore, it is very important to use digital image processing technology and target detection technology to develop high precision, high efficiency and low-cost fabric defect detection model suitable for application deployment. Driven by deep learning-based neural network technology, many models with excellent performance have emerged in the field of object detection. This paper will improve on the basis of YOLOv5s model. ACmix module is introduced in Backbone layer of YOLOv5s model. This module overcomes the problem that convolution operation may cause local information loss after feature extraction, improves the model's limitation on feature extraction of complex background images, and enhances the model's processing ability in local feature fusion. Compared with the original YOLOv5s algorithm, mAP has improved by 0.6%. In the complex background, the detection accuracy of fabric defects targets has been significantly optimized and improved.


LIN S, HE Z, SUN L. Self-Transfer Learning Network for Multicolor Fabric Defect Detection[J]. Neural Processing Letters, 2022.

ZHANG J, JING J, LU P, et al. Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing[J]. Measurement, 2022, 201:111665.

LIU Q, WANG C, LI Y, et al. A Fabric Defect Detection Method Based on Deep Learning[J]. IEEE Access, 2022,10({}): 4284-4296.

JING J, ZHUO D, ZHANG H, et al. Fabric defect detection using the improved YOLOv3 model[J]. Journal of Engineered Fibers and Fabrics, 20,15({}): 2078651668.

JUN X, WANG J, ZHOU J, et al. Fabric defect detection based on a deep convolutional neural network using a two-stage strategy[J]. Textile Research Journal, 2021,91(1-2): 130-142.

RONG-QIANG L, MING-HUI L, JIA-CHEN S, et al. Fabric Defect Detection Method Based on Improved U-Net[J]. Journal of Physics: Conference Series, 2021,1948(1): 12160.

ELEMMI M C, ANAMI B S, MALVADE N N. Defective and nondefective classif ication of fabric images using shallow and deep networks[J]. International Journal of Intelligent Systems, 2022,37(3): 2293-2318.

Wu Zhiyang, Zhuo Yong, Li Jun, et al. Convolutional Neural Network based Fast Defect Detection Algorithm for Monochromatic fabric [J]. Journal of Computer-Aided Design and Graphics, 2018,30(12): 2262-2270.

OUYANG W, XU B, HOU J, et al. Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network[J]. IEEE Access, 2019,7({}): 70130-70140.

PAN X, GE C, LU R, et al. On the Integration of Self-Attention and Convolution[Z]. 2022.







How to Cite

Kong, Z. (2024). YOLOv5s fabric defect detection model with mixed attention mechanism. Journal of Computing and Electronic Information Management, 12(2), 55-59. https://doi.org/10.54097/

Similar Articles

1-10 of 58

You may also start an advanced similarity search for this article.