DCC-CenterNet: A rapid detection method for steel surface defects

Article Engineering, Multidisciplinary

A convolutional neural network-based method for workpiece surface defect detection

Junjie Xing et al.

Summary: An automatic detection method based on convolutional neural networks is proposed in this paper and its detection performance is evaluated and compared with other models, showing that the method has better performance in real-time automatic detection of workpiece surface defects.

MEASUREMENT (2021)

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Article Engineering, Multidisciplinary

Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN

Yingying Xu et al.

Summary: This paper proposes a novel tunnel defect inspection method based on Mask R-CNN, with detailed studies on PAFPN and the edge detection branch, showing their robustness and accuracy in tunnel defect detection and segmentation.

MEASUREMENT (2021)

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Article Engineering, Multidisciplinary

Deep learning-based fast recognition of commutator surface defects

Yu Feng Shu et al.

Summary: This paper proposes a method for detecting and identifying commutator surface defects, by applying YOLOv3 target detection to commutator surface defect detection and recognition, designing a network with smaller model size, fewer parameters, and faster running time, and achieving good accuracy in experiments.

MEASUREMENT (2021)

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Article Geochemistry & Geophysics

Ship Detection in Large-Scale SAR Images Via Spatial Shuffle-Group Enhance Attention

Zongyong Cui et al.

Summary: A ship detection method in large-scale SAR images via CenterNet is proposed, which defines the target as a point and locates the center point of the target through key point estimation to avoid missing small targets, and reduces false alarms through the introduction of SSE attention module. Experimental results show that the proposed method can detect all targets in dense-docking conditions.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Engineering, Electrical & Electronic

An Accurate and Real-Time Surface Defects Detection Method for Sawn Lumber

Yaxin Tu et al.

Summary: This article proposes a surface defects detection method based on improved YOLOv3 for sawn lumbers, which uses Gaussian YOLOv3 and CIoU loss function to improve detection accuracy and reduce repeated detection issues. Two datasets containing different kinds of defects were established, and the effectiveness of the proposed method was validated through experiments.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic