{"title":"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","authors":"Shuai Hao;Tianqi Li;Wei Li;Tianrui Qi;Xu Ma","doi":"10.1109/JSEN.2025.3558229","DOIUrl":null,"url":null,"abstract":"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%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"19748-19758"},"PeriodicalIF":4.3000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Few-Shot Object Detection in Unmanned Aerial Vehicles-Based Transmission Line Inspection: A Method Based on Transfer Learning and Attention Mechanism\",\"authors\":\"Shuai Hao;Tianqi Li;Wei Li;Tianrui Qi;Xu Ma\",\"doi\":\"10.1109/JSEN.2025.3558229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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%.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 11\",\"pages\":\"19748-19758\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10964059/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10964059/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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%.
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