ABSTRACT
Vacuum gauges are the key equipment in vacuum inspection equipment, and the surface defects of vacuum gauges will directly affect the inspection performance and service life of vacuum inspection equipment. At present, the surface defect detection of vacuum gauges mainly relies on the visual inspection of workers, which is less efficient and accurate, and the workers are prone to misjudge the products due to subjective factors. To solve the problems of traditional manual inspection, this paper proposes an improved vacuum gauge surface defect detection method based on the YOLOv5s model called VAG-YOLO. we add a multi-scale adaptive fusion structure (MAF) to the YOLOv5s model to make full use of adaptive fusion of features at different scales to improve the detection performance of the network and increase the defect detection accuracy; Meanwhile, the transformer bottleneck structure (BoT) is introduced to combine multi head Self- Attention (MHSA) with convolutional neural network (CNN) to achieve the effect of reducing the number of network parameters and improving the detection speed. The experimental results show that the average detection accuracy of the VGA-YOLO model is 83.4%, which is higher and faster than the detection accuracy of various other algorithms, and can detect vacuum gauge surface defects in real time.
- Kim C W, Koivo A J. Hierarchical classification of surface defects on dusty wood boards[J]. Pattern Recognition Letters, 1994, 15(7): 713-721.Google ScholarDigital Library
- Aghdam S R, Amid E, Imani M F. A fast method of steel surface defect detection using decision trees applied to LBP based features[C]//2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2012: 1447-1452.Google Scholar
- Bai X, Fang Y, Lin W, Saliency-based defect detection in industrial images by using phase spectrum[J]. IEEE Transactions on Industrial Informatics, 2014, 10(4): 2135-2145.Google ScholarCross Ref
- Choi D, Jeon Y, Yun J P, Pinhole detection in steel slab images using Gabor filter and morphological features[J]. Applied Optics, 2011, 50(26): 5122-5129.Google ScholarCross Ref
- Wang H, Zhang J, Tian Y, A simple guidance template-based defect detection method for strip steel surfaces[J]. IEEE Transactions on Industrial Informatics, 2018, 15(5): 2798-2809.Google ScholarCross Ref
- Tsanakas J A, Chrysostomou D, Botsaris P N, Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements[J]. International Journal of Sustainable Energy, 2015, 34(6): 351-372.Google ScholarCross Ref
- Zhao Z, Gui K, Wang P. Fabric defect detection based on cascade faster R-CNN[C]//Proceedings of the 4th International Conference on Computer Science and Application Engineering. 2020: 1-6.Google Scholar
- Hu B, Wang J. Detection of PCB surface defects with improved faster-RCNN and feature pyramid network[J]. Ieee Access, 2020, 8: 108335-108345.Google ScholarCross Ref
- Li W, Dong T, Shi H, Defect detection algorithm of wire rope based on color segmentation and Faster RCNN[C]//2021 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2021: 656-661.Google Scholar
- Cui L, Jiang X, Xu M, SDDNet: a fast and accurate network for surface defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13.Google ScholarCross Ref
- Yao Y, Wei S, Wang J. Surface Defect Detection of Aircraft Flared Duct Based on Improved YOLOv4 Algorithm[C]//2021 4th International Conference on Signal Processing and Machine Learning. 2021: 26-32.Google Scholar
- Anwar N, Shen Z, Wei Q, YOLOv4 Based Deep Learning Algorithm for Defects Detection and Classification of Rail Surfaces[C]//2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021: 1616-1620.Google Scholar
- Zheng L, Wang X, Wang Q, A Fabric Defect Detection Method Based on Improved YOLOv5[C]//2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021: 620-624.Google Scholar
- Feng Z, Guo L, Huang D, Electrical Insulator Defects Detection Method Based on YOLOv5[C]//2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2021: 979-984.Google Scholar
- Xu Y, Zhang K, Wang L. Metal Surface Defect Detection Using Modified YOLO[J]. Algorithms, 2021, 14(9): 257.Google ScholarCross Ref
- Zheng J, Han Q, Wang L, Lightweight Bimodal Rail Surface Defect Detection Method: Parallel-YOLOv4-Tiny[C]//2021 The 5th International Conference on Video and Image Processing. 2021: 98-103.Google Scholar
- Srinivas A, Lin T Y, Parmar N, Bottleneck transformers for visual recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 16519-16529.Google Scholar
- Liu S, Huang D, Wang Y. Learning spatial fusion for single-shot object detection[J]. arXiv preprint arXiv:1911.09516, 2019.Google Scholar
- Liu W, Anguelov D, Erhan D, Ssd: Single shot multibox detector[C]//European conference on computer vision. Springer, Cham, 2016: 21-37.Google Scholar
- Lin T Y, Goyal P, Girshick R, Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980-2988.Google Scholar
- Ren S, He K, Girshick R, Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.Google Scholar
Index Terms
- A Surface Defect Detection method for vacuum gauges based on VAG-YOLO
Recommendations
An Improved YOLOv5 with Structural Reparameterization for Surface Defect Detection
Artificial Neural Networks and Machine Learning – ICANN 2023AbstractSurface defects produced by the manufacturing process directly degrades the quality of industrial materials such as hot-rolled steel. However, existing methods for detecting surface defects cannot meet the requirements in terms of speed and ...
Steel Surface Defect Detection Based on SSAM-YOLO
The defect inspection of the steel surface is crucial to modern manufacturing and highly depends on inefficient manual work. The emergence of deep learning has prompted the development of automated defect detection methods, but the current methods ...
Defect Detection of Production Surface Based on CNN
Green, Pervasive, and Cloud ComputingAbstractWith the continuous development of artificial intelligence, great progress has been made in the field of object detection. Defect detection is a branch of the field of object detection, as long as the purpose is to locate and classify defects on ...
Comments