基于转移学习和注意机制的无人机输电线路检测中的小目标检测方法

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Shuai Hao;Tianqi Li;Wei Li;Tianrui Qi;Xu Ma
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引用次数: 0

摘要

在利用无人机对输电线路进行巡检的过程中,由于采样不足、背景复杂,可能导致故障检测质量不高,影响电力系统的正常运行。为此,设计了基于迁移学习和注意机制的多尺度故障目标检测网络(TLAM-Det),该网络包括特征预提取网络(FPENet)和基于稀疏特征提取和注意机制的故障目标检测网络(SAMNet)。具体而言,设计了多尺度特征提取模块(INR-Block)来构建FPENet,有效地从源数据中预学习故障特征。然后,利用迁移学习方法将预训练好的权值和参数转移到SAMNet中,以提高SAMNet的特征提取能力。为了增强图像特征压缩能力,抵抗复杂背景干扰,设计了一种基于注意机制的空间特征提取与融合模块。最后,进行了大量的实验来评估tam - det的检测性能。将实验结果与10种最先进的SOTA检测方法进行比较,获得了最高的平均精度(mAP),达到94.4%,平均提高9.77%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Few-Shot Object Detection in Unmanned Aerial Vehicles-Based Transmission Line Inspection: A Method Based on Transfer Learning and Attention Mechanism
During the operation of utilizing unmanned aerial vehicles (UAVs) to inspect transmission lines, insufficient samples and complex backgrounds may lead to poor detection quality of faults, which affects the normal operation of the power system. Therefore, a multiscale fault object detection network based on transfer learning and attention mechanism (TLAM-Det) is designed, which includes a feature preextraction network (FPENet) and a fault object detection network based on sparse feature extraction and attention mechanism (SAMNet). Specifically, a multiscale feature extraction module (INR-Block) is designed to construct FPENet, which effectively prelearns fault features from the source data. Then, to solve the low precision caused by few-shot data, the transfer learning are utilized to transfer the pretrained weights and parameters to SAMNet, which aims to improve the feature extraction capability. A spatial feature extraction and fusion module (SAMF) based on attention mechanism is designed to enhance the feature compression capability and resist complex background interference. Finally, extensive experiments were conducted to evaluate the detection performance of TLAM-Det. The experimental results were compared with ten state-of-the-art (SOTA) detection methods and achieved the highest mean average precision (mAP) of 94.4%, which is an average increase of 9.77%.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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