Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Choi, Jiho; Lee, Sang Jun

doi:10.3390/rs14143421

Cited by 4 publications

(3 citation statements)

References 60 publications

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“…By leveraging multiple levels of information, the network can automatically learn how to combine them and generate satisfactory fused outputs. In recent work, Choi et al [14] attempted to generate the location information of power lines in input images by introducing attention into a two-stage semi-supervised learning framework. In the first stage of their method, they utilized the information from various layers of the VGG network to form an Attention Localization Mask (ALM); in the second stage, the mask and subnetwork were used to generate the contour information of the power lines.…”

Section: Power Line Segmentationmentioning

confidence: 99%

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Yan,

Zhang,

Shen

et al. 2023

Drones

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Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.

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Section: Power Line Segmentationmentioning

confidence: 99%

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Yan,

Zhang,

Shen

et al. 2023

Drones

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“…Ref. [8] focuses on weakly supervised learning to detect transmission lines in UAV imagery. This study introduces a novel algorithm based on unpaired image-to-image translation that requires only image-level labels.…”

Section: Object Detection and Trackingmentioning

confidence: 99%

Editorial for Special Issue: “Recent Progress in UAV-AI Remote Sensing”

Dong,

Yang,

Laneve

et al. 2023

Remote Sensing

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“…According to [21][22][23][24][25][26][27][28], WSOD in RSIs still encounters two major challenges. First of all, most previous methods tend to focus on the most discriminative regions in an image (part domination).…”

Section: Introductionmentioning

confidence: 99%

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

Zheng,

Wu,

et al. 2024

Remote Sensing

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Weakly supervised object detection (WSOD) aims to predict a set of bounding boxes and corresponding category labels for instances with only image-level supervisions. Compared with fully supervised object detection, WSOD in remote sensing images (RSIs) is much more challenging due to the vast foreground-related context regions. In this paper, we propose a progressive image-level and instance-level feature refinement network to address the problems of missing detection and part domination for WSOD in RSIs. Firstly, we propose a multi-label attention mining loss (MAML)-guided image-level feature refinement branch to effectively allocate the computational resources towards the most informative part of images. With the supervision of MAML, all latent instances in images are emphasized. However, image-level feature refinement further expands responsive gaps between the informative part and other sub-optimal informative ones, which results in exacerbating the problem of part domination. In order to alleviate the above-mentioned limitation, we further construct an instance-level feature refinement branch to re-balance the contributions of different adjacent candidate bounding boxes according to the detection task. An instance selection loss (ISL) is proposed to progressively boost the representation of salient regions by exploring supervision from the network itself. Finally, we integrate the image-level and instance-level feature refinement branches into a complete network and the proposed MAML and ISL functions are merged with class classification and box regression to optimize the whole WSOD network in an end-to-end training fashion. We conduct experiments on two popular WSOD datasets, NWPU VHR-10.v2 and DIOR. All the experimental results demonstrate that our method achieves a competitive performance compared with other state-of-the-art approaches.

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Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Cited by 4 publications

References 60 publications

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Editorial for Special Issue: “Recent Progress in UAV-AI Remote Sensing”

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

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