Research on computer vision-based electrical energy measurement equipment recognition and thermal anomaly detection methods

Abstract

The aim of this paper is to propose a deep learning-based method for pedestrian re-identification, which achieves recognition across different cameras, time periods, and scenes, while maintaining high accuracy and efficiency in inference and speed. Firstly, a powerful convolutional neural network is employed to extract features from pedestrian images, and a triplet loss function is utilized to learn these features. Then, an average feature vector of retrieved images is used to construct a feature database. Finally, during cross-camera image retrieval, the cosine similarity metric is employed to calculate the distance between feature vectors, and some techniques are applied to accelerate the speed of image retrieval. Experimental results demonstrate that the proposed method achieves accurate and efficient pedestrian re-identification, meeting the requirements of real-time performance and scalability, and holds practical value.