An industrial part recognition algorithm combining multiscale feature extraction and lightweight improvement

Jie Zhao; Xiaojuan Hu; Yichen Wang

doi:10.1117/12.3048210

3 October 2024 An industrial part recognition algorithm combining multiscale feature extraction and lightweight improvement

Jie Zhao, Xiaojuan Hu, Yichen Wang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720C (2024) https://doi.org/10.1117/12.3048210
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Aiming at the problems of low accuracy and large number of parameters of the current general-purpose detection methods in coping with the tasks of complex background, part occlusion and multi-scale part recognition, a lightweight industrial part detection algorithm is proposed on the basis of YOLOv8s. Designing C2f-DRRCB efficient feature extraction module in the backbone network to improve the multi-scale feature fusion performance; by introducing a lightweight Adown downsampling module, more feature-critical information is retained while reducing the number of model parameters and computation; the EMA multi-scale attention mechanism is added at the end of the backbone network to improve the network's attention to the important feature regions and reduce the background information's interference; Design of a new lightweight inspection head, SCAD-Head, to improve accurate localization of part targets by capturing long range dependencies and shared convolution. The improved algorithm is applied to the SIP-17 industrial part synthetic dataset for validation, and the results show that compared with the baseline YOLOv8s model, the average detection accuracy of the improved model is improved by 5.7%, the number of parameters is reduced by 35.85%, and the amount of computation is reduced by 27.72%, which is also a certain degree of improvement in the detection performance compared with the other latest detection algorithms.

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

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Jie Zhao, Xiaojuan Hu, and Yichen Wang "An industrial part recognition algorithm combining multiscale feature extraction and lightweight improvement", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720C (3 October 2024); https://doi.org/10.1117/12.3048210

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KEYWORDS

Feature extraction

Data modeling

Detection and tracking algorithms

Convolution

Head

Performance modeling

Inspection

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