Surface crack detection by magnetic particle inspection (in German) Goebbels, K. Materialprufung, vol. 30, no. 10, pp. 327–332 (Oct. 1988)

In the paper, a permanent magnet adsorption wall-climbing robot using magnetic particle detection technology for crack detection is introduced, which solves the problems of low efficiency of traditional manual detection and long detection time. According to the working environment of the detection system and the detection functions that need to be completed, the body structure of the robot is designed, the overall size of the robot is smaller than the distance between two steam turbine blades, so it can achieve the crack detection function of large steam turbine blades, and the stability and force analysis of the robot are carried out, and the adsorption conditions that meet the conditions of no sliding and overturning are obtained. In the paper, we use the magnetic circuit method to design a miniature excitation device for robotic applications and use the simulation software Ansoft-Maxwell to verify its feasibility. In the final experiment, it can be shown that the robot designed can achieve a series of functions such as magnetic particle inspection and image acquisition. There is a good prospect for the inspection of turbine blades.

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Convolutional Neural network based Online Rail surface Crack Detection

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) ◽

10.1109/iciccs51141.2021.9432080 ◽

2021 ◽

Author(s):

Chitturi Akhila ◽

Cheemala Astha Diamond ◽

A. Mary Posonia

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Surface Crack ◽

Crack Detection

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CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽

10.3390/electronics10121402 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1402

Author(s):

Taehee Lee ◽

Yeohwan Yoon ◽

Chanjun Chun ◽

Seungki Ryu

Keyword(s):

Surface Crack ◽

Autonomous Vehicles ◽

Crack Detection ◽

Model Performance ◽

Semantic Segmentation ◽

Road Surface ◽

Image Brightness ◽

The Road ◽

Detection Model ◽

Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

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