Prof. Jianqiang Guo Profile

Prof. Jianqiang Guo

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Proceedings Article | 24 November 2023 Paper

Multi-feature fusion weld crack segmentation method based on visible image detection and ECPT

Tianxiang Wang, Jianping Peng, Jianqiang Guo, Kang Tian

Proceedings Volume 12935, 129351Q (2023) https://doi.org/10.1117/12.3006334

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Due to the wide application of welding in the modern industry, effective detection of weld surface defects is an important measure to ensure the quality of components, monitor the Service life of the structure, and ensure the safety of users. However, there are wrinkles and stains on the weld surface, which makes detection difficult. Based on the dynamic detection of pulsed eddy current thermography, a multi-feature fusion algorithm of infrared features and visible information is proposed in this paper. In dynamic detection, the relative position of cracks in the field of view is constantly changing, therefore, the thermal image sequences are spatially aligned to obtain the transient thermal response curve in static mode. Feature extraction and dimensionality reduction of thermal image sequences are carried out in time domain. The processed data is fused with the visible image features, and classified in pixel-level applying the pattern recognition network. The experimental results show that the proposed algorithm can effectively suppress the noise caused by weld texture and surface stains, and obtain more clear and accurate defect information. All 21 weld surface defects can be detected, and the detection ability is greatly improved.

Proceedings Article | 24 November 2023 Paper

Defect detection and recognition technology in wheel images based on deep learning

Yanqing Zhang, Xiaorong Gao, Jianping Peng, Jianqiang Guo

Proceedings Volume 12935, 129351N (2023) https://doi.org/10.1117/12.3006110

KEYWORDS: Ultrasonics, Defect detection, Object detection, Feature fusion, Performance modeling, Feature extraction, Data modeling, Detection and tracking algorithms, Deep learning

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The ultrasonic detection technology of railway locomotive wheels is of great significance to the safety of train operation. However, the current detection technology relies on experts ' manual operation, which has the disadvantages of low accuracy and high cost. In this paper, a detection method based on improved Exceeding YOLO Series in 2021 (YOLOX) is proposed. Firstly, a series of processing such as cutting and rotating the ultrasonic B-scan image obtained by LU system is carried out to obtain the B-scan data set of wheel ultrasonic defects after data enhancement. Secondly, we add an adaptive spatial feature fusion block (ASFF) to the tail of the Neck module of the YOLOX detection algorithm, and further improve the multi-scale feature map. Finally, the original BCEWithLogitsLoss in the loss function is replaced by FocalLoss to improve the ability to distinguish defects from similar backgrounds. The test results show that the detection rate of each type of the improved YOLOX model is more than 90 %, the false negative rate and false positive rate are less than 10 %, and the detection speed is 17 ms. Compared with the original YOLOX network and other mainstream detection models, the improved YOLOX model has the best detection performance. This study provides a new idea for the automation of ultrasonic testing of train wheels.

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