Robust Object Recognition in Open Environments Based on Causal Inference

The rapid development of artificial intelligence technology has drawn widespread attention to the problem of target recognition in open environments. Traditional target recognition methods are easily affected by environmental changes and interference noise, leading to insufficient recognition accuracy and robustness. This paper proposes a robust target recognition method based on causal inference, aiming to improve the robust performance of image recognition models in open environments. The method first extracts features from images, analyzes the impact of noise features such as the environment in image features, and derives the essential feature properties of the target. Then, the causal relationship is introduced into the target recognition, and by analyzing the relationship between the environment, the target, and the observation data, the features of the target are combined with the observation data to achieve accurate and stable recognition of the target. The proposed method has achieved good target recognition effects on multiple open datasets, and the experimental results show that the algorithm can effectively solve the problem of poor robustness of the target recognition model in open environments, providing strong support for practical applications. Through the application of this method, the stability and robustness of the target recognition model can be significantly improved, which has important practical application value.