Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust.

Abstract

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.

Plant phenotyping relevance

植物のさび病画像から病斑をセグメンテーションするための適応的画像拡張法をDRLで開発・評価しており、植物病害状態の画像ベース表現型取得が中心である。

Code and data availability

The paper's authors publicly released their DRL adaptive image augmentation analysis code on GitHub, and the plant rust leaf image dataset used for phenotyping/segmentation is publicly available on Baidu AI Studio per the data availability statement.