A two-stage deep learning method for foreign object detection and localization

Yuwei Zhang; Miaolong Yuan; Zhenbiao Wang

doi:10.1117/12.3024079

15 February 2024 A two-stage deep learning method for foreign object detection and localization

Yuwei Zhang, Miaolong Yuan, Zhenbiao Wang

Proceedings Volume 13069, International Conference on Optical and Photonic Engineering (icOPEN 2023); 130690G (2024) https://doi.org/10.1117/12.3024079
Event: International Conference on Optical and Photonic Engineering (icOPEN 2023), 2023, Singapore, Singapore

Abstract

Foreign object detection and localization is of critical importance in various real-world scenarios. For example, a foreign object in an automatic assembly line could result in severe dangers. In this paper, we propose a two-stage deep learning method to identify and localize foreign objects in a working environment. It includes two major stages, i.e., detection and localization. In the detection stage, an advanced anomaly detection model is first used to identify unknown object candidates. However, the unknown objects detected in this stage might include potential normal classes. Subsequently, we use the working environment data to train a YOLO model to filter out the false positives in the potential unknown objects. In the localization stage, we use K-Means++ to cluster a heatmap generated in the first stage and extract activation points with highest activation scores which are fed into an advanced segmentation model for accurate segmentation and localization of foreign objects. We have conducted experiments to validate the performance of the method in an experimental setup. The developed mode can well adapt to various scenarios in manufacturing automation.

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

Citation Download Citation

Yuwei Zhang, Miaolong Yuan, and Zhenbiao Wang "A two-stage deep learning method for foreign object detection and localization", Proc. SPIE 13069, International Conference on Optical and Photonic Engineering (icOPEN 2023), 130690G (15 February 2024); https://doi.org/10.1117/12.3024079

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KEYWORDS

Object detection

Data modeling

Education and training

Image segmentation

Deep learning

Image restoration

Feature extraction

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