Transmission line surface defect detection method based on uav autonomous inspection

Xie, Xu

doi:10.1088/1742-6596/2132/1/012030

Cited by 1 publication

(1 citation statement)

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“…Since surface defects can be detected through visual inspection, computer-vision-based artificial intelligence (AI) methods have become increasingly popular for identifying and classifying these defects. For instance, algorithms such as You Only Look Once (YOLO) and region-based convolutional neural networks (RCNNs) have been developed to detect and segment defects in real time on low-shot walls, steel plates, and even transmission lines [ 6 , 7 , 8 ]. However, as these algorithms rely on supervised learning, there is a challenge in collecting a sufficient amount of image data—particularly for defective samples.…”

Section: Introductionmentioning

confidence: 99%

Image Generation and Recognition for Railway Surface Defect Detection

Xia

Han

Kwon

2023

Sensors

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Railway defects can result in substantial economic and human losses. Among all defects, surface defects are the most common and prominent type, and various optical-based non-destructive testing (NDT) methods have been employed to detect them. In NDT, reliable and accurate interpretation of test data is vital for effective defect detection. Among the many sources of errors, human errors are the most unpredictable and frequent. Artificial intelligence (AI) has the potential to address this challenge; however, the lack of sufficient railway images with diverse types of defects is the major obstacle to training the AI models through supervised learning. To overcome this obstacle, this research proposes the RailGAN model, which enhances the basic CycleGAN model by introducing a pre-sampling stage for railway tracks. Two pre-sampling techniques are tested for the RailGAN model: image-filtration, and U-Net. By applying both techniques to 20 real-time railway images, it is demonstrated that U-Net produces more consistent results in image segmentation across all images and is less affected by the pixel intensity values of the railway track. Comparison of the RailGAN model with U-Net and the original CycleGAN model on real-time railway images reveals that the original CycleGAN model generates defects in the irrelevant background, while the RailGAN model produces synthetic defect patterns exclusively on the railway surface. The artificial images generated by the RailGAN model closely resemble real cracks on railway tracks and are suitable for training neural-network-based defect identification algorithms. The effectiveness of the RailGAN model can be evaluated by training a defect identification algorithm with the generated dataset and applying it to real defect images. The proposed RailGAN model has the potential to improve the accuracy of NDT for railway defects, which can ultimately lead to increased safety and reduced economic losses. The method is currently performed offline, but further study is planned to achieve real-time defect detection in the future.

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Section: Introductionmentioning

confidence: 99%