| Issue |
E3S Web Conf.
Volume 522, 2024
2023 9th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2023)
|
|
|---|---|---|
| Article Number | 01023 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202452201023 | |
| Published online | 07 May 2024 | |
Machine vision recognition system for aerospace machined parts based on edge detection
1 School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110000, People’s Republic of China
2 School of Information Science and Engineering, Northeastern University, Shenyang 110000, People’s Republic of China
* Corresponding author: songkc@me.neu.edu.cn
Aerospace T-shaped machined parts are varied and have small structural differences. Manual identification has the problems of low efficiency and low accuracy. In order to realize efficient and accurate classification of aerospace machining parts, we built an image acquisition platform. To improve the edge detail extraction capability, we improved the edge detection algorithm based on deep learning. Furthermore, we employed the VisionTrain software to train recognition classification models for both large classes and subclasses. We then established a cross-granularity image classification process using VisionMaster software. Experimental results show that the improved edge detection algorithm in this paper is better than the existing common algorithm. The system achieves the goal of quickly and accurately recognizing all 60 machined parts.
Key words: Machine vision / Aeronautical machining parts / Edge detection / Recognition and classification
© The Authors, published by EDP Sciences, 2024
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