Self-Supervised Learning-Based Algorithm for Chinese Image Caption Generation in Electric Robot Inspection Processing

Electric robot will obtain a large amount of image information during inspection, and if these images are checked whether there are faults in power inspection is time-consuming and labor-intensive. There is an urgent need for power image Chinese title generation technology to solve it. However, existing image Chinese title generation methods face the problems of small training data sets, differences in specific applications, and few methods for generating Chinese titles for power images. To this end, this paper proposes a self-supervised learning-based image Chinese title generation algorithm for fault detection in electric robot inspection. Specifically, a contrastive learning-based model to automatically capture the semantic relationship between images and text. Then, we propose an end-to-end encoding-decoding model combined with an attention mechanism to obtain Chinese title generation for inspection images. The effectiveness of the proposed algorithm is experimentally verified on two real datasets.