Fang, F., Xu, Q., & Lim, J.-H. (2022). Hierarchical Defect Detection Based On Reinforcement Learning. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9897947
Abstract:
In this paper, we propose a novel reinforcement learning (RL) based method for defects detection in high-resolution (HR) images. e.g. cracks and scratches on the surfaces of buildings, constructions, and products. Our innovation leverages RL to explore challenging images in progressive manner, using pretrained deep learning (DL) detection as feedback mechanism. First, The DL model is pre-trained on low resolution (LR) images with relatively high defect background ratio (DBR). The RL agent is trained by optimizing a policy network according to feedback of DL model on selected regions of HR images with fairly low DBR to coarsely predict defective region by executing two actions: defective region selection and region refinement. Then, the selected defective regions are evaluated using the DL model to generate final defect region which will be mapped back to the HR images. Experimental results on HR crack and scratch images indicate that our method is able to achieve state-of-the-art performance with 0.976 and 0.965 F1-score respectively.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
This research / project is supported by the Agency for Science, Technology and Research - Feasibility study project
Grant Reference no. : FS-2021-027