Two-stage workpiece recognition algorithm based on Mask-RCNN and template matching

Saiwen Wang

doi:10.1117/12.3055508

12 December 2024 Two-stage workpiece recognition algorithm based on Mask-RCNN and template matching

Saiwen Wang

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134392W (2024) https://doi.org/10.1117/12.3055508
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

To achieve automated sorting and transportation of workpieces in the factory, AMR is used to assist in workpiece handling. This paper proposes a two-stage Workpiece Recognition Algorithm based on Mask-RCNN and template matching for determining the processing stage of workpieces. First, Mask R-CNN is used for instance segmentation and reverse masking to obtain an image of the target workpiece, which serves as the image to be matched. Relying on the three-view angle of the workpiece's external shape during the processing stage, a template image is created, and SIFT-FLANN is used for image matching to identify the processing stage. Finally, experiments show that the method achieves an accuracy rate of 81.27% for recognizing categories of workpieces, with an average matching time is close to 20ms for identifying process stages of disk and sleeve-type workpieces, and a matching accuracy of 98.75%, allows for accurate determination of workpiece type and processing stage. This provides a basis for AMR to transport workpieces and contributes to improving factory production efficiency and reducing production costs.

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

Citation Download Citation

Saiwen Wang "Two-stage workpiece recognition algorithm based on Mask-RCNN and template matching", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134392W (12 December 2024); https://doi.org/10.1117/12.3055508

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KEYWORDS

Image processing

Image segmentation

Detection and tracking algorithms

Feature extraction

Education and training

Cameras

Deep learning

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