Metal Surface Defect Detection Using Modified YOLO

Xu, Yiming; Zhang, Kai; Wang, Li

doi:10.3390/a14090257

Cited by 47 publications

(21 citation statements)

References 32 publications

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“…The detection of large targets is gradually improving, but there is still a lot of work to be done for the detection of small targets [15]. From an optimization point of view, we offer a solution for metal surface defect detection [16], the improved YOLO-V7 shows better performance than other existing methods.…”

Section: Related Workmentioning

confidence: 99%

Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V7

Wang

Xin

2022

IEEE Access

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During the production of strip steel, there are often some defects on the surface of the product. Therefore, the detection of such defect is the key to produce high-quality products. At the same time, the surface defects of the steel cause huge economic losses to the high-tech industry. A steel surface defect detection algorithm based on improved YOLO-V7 is proposed to address the problems of low detection speed and poor detection accuracy of traditional steel surface defect detection methods. First, we use the de-weighted BiFPN structure to make full use of the deep, shallow and original feature information to strengthen feature fusion, reduce the loss of feature information during the convolution process, and improve the detection accuracy. Secondly, the ECA attention mechanism is combined in the backbone part to strengthen the important feature channels. Finally, the original bounding box loss function is replaced by the SIoU loss function, where the penalty term is redefined by taking the vector angle between the required regressions into account. The experimental results show that the improved model proposed in this paper has higher performance compared with other comparison models. Based on our experiments, the proposed model yields 80.2% mAP on the GC10-DET dataset and 81.9% mAP on the NEU-DET dataset with high detection speed, which is better than other existing models.

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Section: Related Workmentioning

confidence: 99%

Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V7

Wang

Xin

2022

IEEE Access

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“…Almost all of these studies are based on one-stage object detection networks. Xu et al (2021) fused the shallow features of DarkNet53 layer 11, the backbone network of YOLOV3, with the deep features to generate a new feature layer. The experimental results show that the improved YOLOV3 has an mAP of 75.1%, which is highly accurate in locating small defect objects while having some real-time performance.…”

Section: Related Workmentioning

confidence: 99%

Enhanced You Only Look Once X for surface defect detection of strip steel

Zhou

et al. 2022

Front. Neurorobot.

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Using deep learning-based methods to detect surface defects in strip steel can reduce the impact of human factors and lower costs while maintaining accuracy and efficiency. However, the main disadvantages of this method is the inability to tradeoff accuracy and efficiency. In addition, the low proportion of valid information and the lack of distinctive features result in a high rate of missed detection of small objects. In this paper, we propose a lightweight YOLOX surface defect detection network and introduce the Multi-scale Feature Fusion Attention Module (MFFAM). Lightweight CSP structures are used to optimize the backbone of the original network. MFFAM uses different scales of receptive fields for feature maps of different resolutions, after which features are fused and passed into the spatial and channel attention modules in parallel. Experimental results show that lightweight CSP structures can improve the detection frame rate without compromising accuracy. MFFAM can significantly improve the detection accuracy of small objects. Compared with the initial YOLOX, the mAP and FPS were 81.21% and 82.87Hz, respectively, which was an improvement of 4.29% and 12.72Hz. Compared with existing methods, the proposed model has superior performance and practicality, verifying the effectiveness of the optimization method.

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“…With the continuous development of network and the continuous improvement of detection accuracy in recent years, they have also been widely used in the industrial field. 10 –15 For example, Xu 10 et al proposed a metal surface defect detection model based on the improved YOLOv3 model, which has made great progress compared with traditional manual detection in terms of accuracy and real-time performance. Kou 12 et al proposed a YOLOv3-based strip steel surface defect detection model.…”

Section: Introductionmentioning

confidence: 99%

A lightweight model for digital printing fabric defect detection based on YOLOX

Su,

Zhang,

et al. 2023

Journal of Engineered Fibers and Fabrics

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Online detection of digital printing defects is a necessary but challenging topic. The performance of the current detection methods is still not ideal for the diversified patterns of digital printing fabric defects and the realtime requirements of online detection. In this paper, we proposed a lightweight model of digital printing fabric defect detection based on YOLOX. Firstly, according to the characteristics of many types of defects and complex background of digitally printed fabrics, a defect detection network structure based on YOLOX is constructed. Then, the SE attention module is introduced to enhance important features and weaken unimportant features, which make the extracted features more directional. And it can further solve the influence of small feature size on the detection accuracy of small targets. The experimental results show that the proposed model has a detection accuracy of 66.2 mAP on our self-built dataset, which is 2.7 percentage points higher than YOLOX. This method can effectively solve the problem that low detection accuracy of small defects. The proposed model can meet the real-time requirements and improve the detection accuracy of small target defects.

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Metal Surface Defect Detection Using Modified YOLO

Cited by 47 publications

References 32 publications

Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V7

Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V7

Enhanced You Only Look Once X for surface defect detection of strip steel

A lightweight model for digital printing fabric defect detection based on YOLOX

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