Surface Defect Detection of Steel Products Based on Improved YOLOv5

Liu, Yajiao; Wang, Jiang; Yu, Haitao; Li, Fulong; Yu, Lifeng; Zhang, Chunhui

doi:10.23919/ccc55666.2022.9902037

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…Compare with YOLOv4, YOLOv5 has an improvement in speed and accuracy of detecting. Li et al [13] proposed an improved YOLOv5 algorithm for steel surface defects detection. Cao et al [14] proposed a steel surface defect detection method based on the improved YOLOv5 algorithm.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…Here, TP is the number of correctly detected defective samples, FP is the number of non-defect samples that are detected, FN is the number of defective samples that are detected wrong. In detail, the mAP is calculated by using formula (13).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…In addition, we also select one-stage detection algorithms as reference methods. They are SSD [12], YOLOv5 [31], YOLOv5s-GSD [13], YOLOv5-Cao [14], and RDD-YOLO [15]. For a more comprehensively comparison, we also add YOLOv7 [46] and YOLOv8 as reference methods.…”

Section: Reference Modelsmentioning

confidence: 99%

“…This method utilized Single Shot multiBox Detector (SSD) [12] network with cross feature fusion. Li et al [13] proposed an improved detection algorithm based on YOLOv5 for steel surface defects detection. Cao et al [14] also proposed a method based on YOLOv5 algorithm.…”

Section: Introductionmentioning

confidence: 99%

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ACD‐YOLO: Improved YOLOv5‐based method for steel surface defects detection

Fan,

Wang,

et al. 2023

IET Image Processing

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Since the quality of steel is of paramount importance in modern production, the defects detection of steel surface is significantly crucial. In this field, two‐stage detection algorithms have encountered issues about low detection speed, while one‐stage detection algorithms have room for improvement in detection accuracy. How to trade‐off between accuracy and speed of detection to better meet the demands of industrial production remains a challenge. To address this problem, this paper proposes a You Only Look Once version 5 (YOLOv5)‐based improved method ACD‐YOLO. ACD‐YOLO model incorporates anchors optimization, context augmentation module, and efficient convolution operators. In anchor optimization, the boundaries of anchors are optimized using an improved genetic algorithm. Moreover, to improve detection accuracy, a context augmentation module is incorporated into both the head and the backbone end of the network. Additionally, efficient convolution operators are adopted to address the increase of computation complexity caused by the context augmentation module. Experimental results show that ACD‐YOLO achieves mean average precision of 79.3%, with frames per second of 72. Compared to reference methods, ACD‐YOLO achieves the best balance between accuracy and speed of detection, and is more suitable for practice industrial production.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Reference Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ACD‐YOLO: Improved YOLOv5‐based method for steel surface defects detection

Fan,

Wang,

et al. 2023

IET Image Processing

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“…The method has an average detection accuracy of 89.24% and can detect 25.62 images of 416*416 pixels per second. Li X. et al (2022) achieved a lightweight model by replacing the backbone network of YOLOV5, reducing the size by 10.4%; and introducing an attention module to improve the detection accuracy by 3.3%.…”

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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Improved Plate Defect Detection Algorithm Based on YOLOv5

Wang,

Zheng

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Surface Defect Detection of Steel Products Based on Improved YOLOv5

Cited by 10 publications

References 15 publications

ACD‐YOLO: Improved YOLOv5‐based method for steel surface defects detection

ACD‐YOLO: Improved YOLOv5‐based method for steel surface defects detection

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

Improved Plate Defect Detection Algorithm Based on YOLOv5

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