Study on the detection of viscose filament defects based on improved YOLOv5

Dong Chen; Limin Cai; Peizhi Zhao; Hao Wei; Zhongyuan Lai

doi:10.1117/12.2689415

10 August 2023 Study on the detection of viscose filament defects based on improved YOLOv5

Dong Chen, Limin Cai, Peizhi Zhao, Hao Wei, Zhongyuan Lai

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274846 (2023) https://doi.org/10.1117/12.2689415
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

In the production process of viscose filament, broken filament inspection is the most important part of detecting filament defects. To solve the problem of low speed and accuracy of broken filament detection and improve the online quality inspection system. In this paper, we design a broken filament detection method for viscose filaments based on the improved YOLOv5 algorithm. The GhostNet network structure is introduced to replace and modify the backbone network layer of YOLOv5 to reduce the complexity and computation of the structure and realize the light weight of the overall network structure; the ECA attention mechanism is introduced in the backbone network to enhance the feature perception of the broken filament target and increase the mobility of the feature information in the deep network. The improved YOLOv5 algorithm achieves an average detection accuracy of 93.9% and an average detection speed of 64 FPS in the final experimental results, which is better than the traditional methods of image recognition detection and can meet the realtime detection requirements of broken filament detection in practical engineering.

Citation Download Citation

Dong Chen, Limin Cai, Peizhi Zhao, Hao Wei, and Zhongyuan Lai "Study on the detection of viscose filament defects based on improved YOLOv5", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274846 (10 August 2023); https://doi.org/10.1117/12.2689415

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KEYWORDS

Convolution

Image processing

Detection and tracking algorithms

Data modeling

Education and training

Machine learning

Deep learning

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