YOT-Net: YOLOv3 Combined Triplet Loss Network for Copper Elbow Surface Defect Detection

Xian, Yuanqing; Liu, Guangjun; Fan, Jinfu; Yang, Yu; Wang, Zhongjie

doi:10.3390/s21217260

Cited by 6 publications

(2 citation statements)

References 41 publications

(51 reference statements)

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“…Wang et al [16] investigated the improved YOLOv4 algorithm using a shallow feature enhancement mechanism for the problems of insensitivity to small objects and low detection accuracy in traffic light detection and recognition. Xian et al [17] used a triple loss function in YOT-Net in order to improve defect detection accuracy for copper elbows. Image similarity was used to enhance the feature extraction capability.…”

Section: Introductionmentioning

confidence: 99%

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng,

Cui,

Liu

et al. 2023

Processes

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Under the background of intelligent manufacturing, in order to solve the complex problems of manual detection of metallurgical saw blade defects in enterprises, such as real-time detection, false detection, and the detection model being too large to deploy, a study on a metallurgical saw blade surface defect detection algorithm based on SC-YOLOv5 is proposed. Firstly, the SC network is built by integrating coordinate attention (CA) into the Shufflenet-V2 network, and the backbone network of YOLOv5 is replaced by the SC network to improve detection accuracy. Then, the SIOU loss function is used in the YOLOv5 prediction layer to solve the angle problem between the prediction frame and the real frame. Finally, in order to ensure both accuracy and speed, lightweight convolution (GSConv) is used to replace the ordinary convolution module. The experimental results show that the mAP@0.5 of the improved YOLOv5 model is 88.5%, and the parameter is 31.1M. Compared with the original YOLOv5 model, the calculation amount is reduced by 56.36%, and the map value is increased by 0.021. In addition, the overall performance of the improved SC-YOLOv5 model is better than that of the SSD and YOLOv3 target detection models. This method not only ensures the high detection rate of the model, but also significantly reduces the complexity of the model and the amount of parameter calculation. It meets the needs of deploying mobile terminals and provides an effective reference direction for applications in enterprises.

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Section: Introductionmentioning

confidence: 99%

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng,

Cui,

Liu

et al. 2023

Processes

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“…Ivan Kuric et al [ 39 ] used the improved AlexNet to achieve the automatic target detection of small samples. Xian et al [ 40 ] used a new model of YOLOv3 combined with a triple loss network to improve the feature extraction capability of neural networks and achieve high-performance surface defect detection. Ren et al [ 41 ] proposed a network model for surface defect detection.…”

Section: Introductionmentioning

confidence: 99%

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet

Xue

Zhao

et al. 2022

Sensors

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Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 μm and 100 μm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.

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A Novel Defect Detection Method for Insulators of Power Transmission Line Based on YOLOv5

Cao¹,

Shang²,

Wang³

et al. 2022

Communications in Computer and Information Science

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YOT-Net: YOLOv3 Combined Triplet Loss Network for Copper Elbow Surface Defect Detection

Cited by 6 publications

References 41 publications

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet

A Novel Defect Detection Method for Insulators of Power Transmission Line Based on YOLOv5

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