Re2FAD: A differential image registration and robust image fusion method framework for power thermal anomaly detection

Zhou, Xihong; Liu, Gang; Zhang, Xiangbo; Prasad, Bavirisetti Durga; Gu, Xinjie; Li, Yonghua

doi:10.1016/j.ijleo.2022.168817

Cited by 11 publications

(1 citation statement)

References 28 publications

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“…Then, normalize the grayscale histogram and calculate the cumulative probability distribution for each grayscale level. Next, remap the pixel values in the original image to the equalized grayscale levels based on the cumulative distribution function [5]. Finally, reconstruct the equalized image based on the mapped pixel values.…”

Section: Enhancement Of Imagesmentioning

confidence: 99%

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

Wang,

Yi,

Kong

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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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.

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Section: Enhancement Of Imagesmentioning

confidence: 99%