异构图像融合算法及其在电力设施检测中的应用研究
摘要：目的 电力设施的及时、准确检测对保障能源供应的可靠性至关重要，而单一传感器在电力设施检测中存在一定的局限性，为此，提出了一种基于显著性检测的多尺度特征异构图像融合算法。 方法 采用边缘制导网络从红外图像中提取显著目标，生成显著目标掩模；在每个区域建立特定的损失函数，结合显著目标掩模引导网络进行特征提取；基于特征层次的定向异构融合方法，将不同尺度的深度特征进行定向结合，最大限度地减少信息丢失。 结果 在TNO数据集上进行的主观与客观实验表明，该算法在大多数评估指标上优于其他方法，验证了其在电力设施检测领域应用的有效性。 结论 该算法有效解决了检测率较低和信息丢失的问题，使电力设施的检测更全面准确，对提高电力设备故障检测的准确度和诊断效率具有重要意义。
Abstract:Objectives Timely and accurate detection of power facilities is very important to ensure the reliability of energy supply. However, a single sensor has certain limitations in the detection of power facilities. Therefore, a multi-scale feature heterogeneous image fusion algorithm based on saliency detection was proposed. Methods Firstly, the edge guidance network was used to extract the salient target from the infrared image to generate the salient target mask. Secondly, a specific loss function was established in each region, and the salient target mask was used to guide the network for feature extraction. Finally, a directional heterogeneous fusion method based on feature hierarchy was proposed, which combined the depth features of different scales to minimize information loss. Results Subjective and objective experiments on the TNO dataset show that the algorithm is superior to other methods in most evaluation indicators, which verifies the effectiveness of its application in the field of power facilities detection. Conclusions The algorithm effectively solves the problems of low detection rate and information loss, and makes the detection of power facilities more comprehensive and accurate. It is of great significance to improve the accuracy and diagnostic efficiency of power equipment fault detection.
标题：异构图像融合算法及其在电力设施检测中的应用研究
title:Heterogeneous Image Fusion Algorithm and Its Application in Power Facility Detection Research
作者：贾梦涵, 刘刚, 徐世杰, 吴双应
authors:Menghan JIA, Gang LIU, Shijie XU, Shuangying WU
关键词：电力设施检测,异构图像融合,目标检测,深度学习,
keywords:power facility detection,heterogeneous image fusion,target detection,deep learning,
发表日期：2024-06-30
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