Research and implementation of key intelligent detection technologies based on deep learning

Xiangchun Zhang; Zhaoyang Zeng; Wenshen Peng; Zhenyu Wang; Junyi Chu; Mingxian Wei; Ran Xu

doi:10.1117/12.3055351

12 December 2024 Research and implementation of key intelligent detection technologies based on deep learning

Xiangchun Zhang, Zhaoyang Zeng, Wenshen Peng, Zhenyu Wang, Junyi Chu, Mingxian Wei, Ran Xu

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134391R (2024) https://doi.org/10.1117/12.3055351
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

Given the complexity of quality defects in aviation product development and production, which results in a lack of effective intelligent detection methods, this paper addresses these challenges by systematically reviewing key technologies for intelligent detection based on digital images. The paper summarizes the concepts and implementation methods of intelligent detection for various application scenarios and specific defect characteristics. For aviation castings with complex defect features and stringent detection requirements, a defect feature dataset for digital radiographic inspection of aerospace castings was constructed using data augmentation techniques such as cropping, flipping, overlap cutting, and Mosaic. Subsequently, an improved Mask-RCNN algorithm, incorporating the global feature pyramid network, was designed and optimized. This algorithm was used to test and verify the detection of three types of defects in aviation castings: looseness, cracks, and high-density inclusions. Experimental results demonstrate that the detection accuracy of this optimized algorithm is 93.25%, with a recall rate of 96.51%. Finally, based on the research findings, intelligent detection and evaluation software for aviation castings, grounded in deep learning, was developed and applied to the detection of a specific type of aviation engine blade casting.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangchun Zhang, Zhaoyang Zeng, Wenshen Peng, Zhenyu Wang, Junyi Chu, Mingxian Wei, and Ran Xu "Research and implementation of key intelligent detection technologies based on deep learning", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134391R (12 December 2024); https://doi.org/10.1117/12.3055351

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KEYWORDS

Detection and tracking algorithms

Deep learning

Object detection

Image segmentation

Aerospace engineering

Education and training

Algorithm development

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