Neural Network Architecture Optimization Method for Defect Recognition

This paper presents an approach for defect recognition, where a neural network architecture search technique is employed. The technique utilizes reinforcement learning algorithms to autonomously search for the most optimal neural network structure. The technique aims to address the issues encountered by deep learning models in defect recognition tasks, such as imbalanced data labels, high computational complexity, and the need for many hyperparameter adjustments. The main approach of the technique is to use reinforcement learning algorithms to train agents to generate new network structures within a given search space and evaluate and update these structures based on a predefined reward function. By continuously generating new candidate structures and evaluating and updating them based on the reward function, the agent can adaptively explore and optimize network structures within the search space to improve model performance and accuracy. Compared to traditional manual model design, the neural network architecture search technique based on reinforcement learning offers higher efficiency and accuracy, enabling researchers to automate the design and optimization of deep learning models, reduce design and tuning time and cost, and improve model performance and accuracy to better meet practical application requirements. Ultimately, the agent will return the optimal network structure for defect recognition tasks.