Welding quality detection of metro train body based on ABC mask R-CNN

He, Deqiang; Ma, Rui; Jin, Zhenzhen; Ren, Ruochen; He, Suiqiu; Xiang, Zaiyu; Chen, Yanjun; Xiang, Weibin

doi:10.1016/j.measurement.2023.112969

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…The R-CNN series has excellent precision [39]. Deqiang He et al [40] tested the weld quality of the train body and proposed the ABC mask R-CNN algorithm, consisting of phased array ultrasonic detection, which can achieve good detection accuracy. In the work of Duong Huong Nguyen [41], a faster R-CNN algorithm was introduced to detect structural defects in steel plates.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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A Novel DME-YOLO Structure in a High-Frequency Transformer Improves the Accuracy and Speed of Detection

Kang,

Jiang,

et al. 2023

Electronics

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Traditional YOLO models face a dilemma when it comes to dim detection targets: the detection accuracy increases while the speed inevitably reduces, or vice versa. To resolve this issue, we propose a novel DME-YOLO model, which is characterized by the establishment of a backbone based on the YOLOv7 and Dense blocks. Moreover, through the application of feature multiplexing, both the parameters and floating-point computation were decreased; therefore, the defect detection process was accelerated. We also designed a multi-source attention mechanism module called MSAM, which is capable of integrating spatial information from multiple sources. Due to its outstanding quality, the addition of MSAM as the neck of the original YOLOv7 model compensated for the loss of spatial information in the process of forward propagation, thereby improving the detection accuracy of small target defects and simultaneously ensuring real-time detection. Finally, EIOU was adopted as a loss function to bolster the target frame regression process. The results of the experiment indicated detection accuracy and speed values of up to 97.6 mAP and 51.2 FPS, respectively, suggesting the superiority of the model. Compared with the YOLOv7 model, the experimental parameters for the novel DME-YOLO increased by 2.8% for mAP and 15.7 for FPS, respectively. In conclusion, the novel DME-YOLO model had excellent overall performance regarding detection speed and accuracy.

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Section: Deep Learning Methodsmentioning

confidence: 99%

“…A K-means algorithm was used to cluster the annotation boxes of the dataset, and we obtained nine prior boxes. Their coordinates were (12,16), (19,36), (40,28), (36,75), (76, 55), (72, 146), (142, 110), (192, 243), (459, 401).…”

Section: High-frequency Transformer Defect Datasetsmentioning

confidence: 99%

A Novel DME-YOLO Structure in a High-Frequency Transformer Improves the Accuracy and Speed of Detection

Kang,

Jiang,

et al. 2023

Electronics

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Recognition Method for Train Coupler Handle Based on YOLOv5 Model

Liu,

Li,

et al. 2024

Lecture Notes in Mechanical Engineering

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To solve the problem of identifying different types of car couplers during the operation of the automatic uncoupling robot of a tippler, a method for recognizing the handle of a car coupler based on the YOLOv5 model has been proposed. This method selects YOLOv5n, which is relatively simple in the YOLOv5 series, as the benchmark model for the detection network. The overall structure is more concise, effectively reducing the number of model parameters while ensuring detection accuracy. The YOLOv5n model used for feature extraction and target recognition on two types of coupler datasets: upper action and lower action, greatly reducing the time required for training and testing, and achieving extremely high recognition accuracy. Compared with the commonly used SSD300 model and Faster R-CNN model, it shows significant advantages in terms of parameter quantity, computational complexity, predictive inference speed and weight file size.

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An automatic unsafe states reasoning approach towards Industry 5.0’s human-centered manufacturing via Digital Twin

Wang,

et al. 2024

Advanced Engineering Informatics

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Welding quality detection of metro train body based on ABC mask R-CNN

Cited by 8 publications

References 40 publications

A Novel DME-YOLO Structure in a High-Frequency Transformer Improves the Accuracy and Speed of Detection

A Novel DME-YOLO Structure in a High-Frequency Transformer Improves the Accuracy and Speed of Detection

Recognition Method for Train Coupler Handle Based on YOLOv5 Model

An automatic unsafe states reasoning approach towards Industry 5.0’s human-centered manufacturing via Digital Twin

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