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RSS FeedsRemote Sensing, Vol. 13, Pages 4134: Detection of Abnormal Vibration Dampers on Transmission Lines in UAV Remote Sensing Images with PMA-YOLO (Remote Sensing)

 
 

15 october 2021 13:28:12

 
Remote Sensing, Vol. 13, Pages 4134: Detection of Abnormal Vibration Dampers on Transmission Lines in UAV Remote Sensing Images with PMA-YOLO (Remote Sensing)
 


The accurate detection and timely replacement of abnormal vibration dampers on transmission lines are critical for the safe and stable operation of power systems. Recently, unmanned aerial vehicles (UAVs) have become widely used to inspect transmission lines. In this paper, we constructed a data set of abnormal vibration dampers (DAVDs) on transmission lines in images obtained by UAVs. There are four types of vibration dampers in this data set, and each vibration damper may be rusty, defective, or normal. The challenges in the detection of abnormal vibration dampers on transmission lines in the images captured by UAVs were as following: the images had a high resolution as well as the objects of vibration dampers were relatively small and sparsely distributed, and the backgrounds of cross stage partial networks of the images were complex due to the fact that the transmission lines were erected in a variety of outdoor environments. Existing methods of ground-based object detection significantly reduced the accuracy when dealing with complex backgrounds and small objects of abnormal vibration dampers detection. To address these issues, we proposed an end-to-end parallel mixed attention You Only Look Once (PMA-YOLO) network to improve the detection performance for abnormal vibration dampers. The parallel mixed attention (PMA) module was introduced and integrated into the YOLOv4 network. This module combines a channel attention block and a spatial attention block, and the convolution results of the input feature maps in parallel, allowing the network to pay more attention to critical regions of abnormal vibration dampers in complex background images. Meanwhile, in view of the problem that abnormal vibration dampers are prone to missing detections, we analyzed the scale and ratio of the ground truth boxes and used the K-means algorithm to re-cluster new anchors for abnormal vibration dampers in images. In addition, we introduced a multi-stage transfer learning strategy to improve the efficiency of the original training method and prevent overfitting by the network. The experimental results showed that the for PMA-YOLO in the detection of abnormal vibration dampers reached 93.8% on the test set of DAVD, 3.5% higher than that of YOLOv4. When the multi-stage transfer learning strategy was used, the was improved by a further 0.2%.


 
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Remote Sensing, Vol. 13, Pages 4133: Deep Ensembles for Hyperspectral Image Data Classification and Unmixing (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4136: SAR-Based Flood Monitoring for Flatland with Frequently Fluctuating Water Surfaces: Proposal for the Normalized Backscatter Amplitude Difference Index (NoBADI) (Remote Sensing)
 
 
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