Power Line Segmentation in Aerial Images Using Convolutional Neural Networks

Saurav, Sumeet; Gidde, Prashant Sadashiv; Singh, Sanjay; Saini, Ramesh Kumar

doi:10.1007/978-3-030-34869-4_68

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…The algorithm does not require an extensive learning set, which makes it different from many deep learning methods [37,38], which are currently gaining popularity. Similar to some other solutions [23,35,40,41], wire reconstruction in 3D space is performed using geometry.…”

Section: Discussionmentioning

confidence: 99%

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3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

et al. 2020

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Regular power line inspections are essential to ensure the reliability of electricity supply. The inspections of overground power submission lines include corridor clearance monitoring and fault identification. The power lines corridor is a three-dimensional space around power cables defined by a set distance. Any obstacles breaching this space should be detected, as they potentially threaten the safety of the infrastructure. Corridor clearance monitoring is usually performed either by a labor-intensive total station survey (TS), terrestrial laser scanning (TLS), or expensive airborne laser scanning (ALS) from a plane or a helicopter. This paper proposes a method that uses unmanned aerial vehicle (UAV) images to monitor corridor clearance. To maintain the adequate accuracy of the relative position of wires in regard to surrounding obstacles, the same data were used both to reconstruct a point cloud representation of a digital surface model (DSM) and a 3D power line. The proposed algorithm detects power lines in a series of images using decorrelation stretch for initial image processing, the modified Prewitt filter for edge enhancement, random sample consensus (RANSAC) with additional parameters for line fitting, and epipolar geometry for 3D reconstruction. DSM points intruding into the corridor are then detected by calculating the spatial distance between a reconstructed power line and the DSM point cloud representation. Problematic objects are localized by segmenting points into voxels and then subsequent clusterization. The processing results were compared to the results of two verification methods—TS and TLS. The comparison results show that the proposed method can be used to survey power lines with an accuracy consistent with that of classical measurements.

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Section: Discussionmentioning

confidence: 99%

“…The research also used enhanced dense matching to detect obstacles in the power line corridor. The neural networks were also used successfully for line segmentation in another study [38]. The effectiveness of these algorithms is at least 80%.…”

Section: Introductionmentioning

confidence: 98%

3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

et al. 2020

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“…Saurav al et. use a nested U-Net segmentation architecture to automatic autonomous visual power line segmentation [23]. Currently, the distribution line detection by using deep learning lacks the comprehensive consideration of detection speed and accuracy in application.…”

Section: Research Vision-based Uav Distribution Line Inspection Using Deep Learningmentioning

confidence: 99%

“…Compared with the model with ResNet [28] or Dilated Residual Networks (DRN) [29] as the backbone network, the processing speed is increased by 7-1000 times. Compared with the lane line state-of-the-art network Special CNN (SCNN) [30] and the nested U-Net [31] adopted in [23], the IoU rate and processing speed are significantly improved.…”

Section: Num Classesmentioning

confidence: 99%

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Chen

Zhuang

Wang

et al. 2022

Preprint

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Regular inspection of distribution line is an important link to maintain the normal operation of distribution network. Using unmanned aerial vehicle (UAV) instead of manpower can save the cost of inspection. With the universal application of vision sensor in UAV and the rapid development of deep learning, Convolutional Neural Networks (CNN) is applied to the detection of power line in UAV visible images. In view of the lack of application environment inspection methods for distribution line, a vision-based UAV distribution line inspection method using deep learning and a dataset for the deep learning method of distribution line inspection task are proposed in this paper. The method proposed predicts distribution line area through the encoder-decoder structure network firstly. Image processing operation and sampling clustering are used to remove the interference. Finally, the UAV tracking direction of distribution power line is calculated according to the detected distribution line. The method can reach an inspection speed of nearly 77ms per frame, the range of heading deviation error can reach (-1.52°, 1.36°), the tracking rate nearly 100%. Through the test of network and inspection method on dataset, the results show that the method proposed in this paper can be effectively, quickly and accurately applied to UAV distribution line inspection.

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“…VGG16 architecture [32] is modified based on richer convolutional features [33] to evaluate both datasets. The dataset in [27] includes 530 PL images captured by UAV with the resolution of 5,472×3,078 pixels. These images are manually cropped and divided into nonoverlapped patches with the size of 512×512 pixels.…”

Section: Datasets Based On Pls Images Onlymentioning

confidence: 99%

TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines

Rabab

Wang

2020

Preprint

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Accurate detection and segmentation of transmission towers (TTs) and power lines (PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety. Meanwhile, aerial images of TTs and PLs pose a number of new challenges to the computer vision researchers who work on object detection and segmentation -PLs are long and thin, and may show similar color as the background; TTs can be of various shapes and most likely made up of line structures of various sparsity; The background scene, lighting, and object sizes can vary significantly from one image to another. In this paper we collect and release a new TT/PL Aerial-image (TTPLA) dataset, consisting of 1,100 images with the resolution of 3,840×2,160 pixels, as well as manually labeled 8,987 instances of TTs and PLs. We develop novel policies for collecting, annotating, and labeling the images in TTPLA. Different from other relevant datasets, TTPLA supports evaluation of instance segmentation, besides detection and semantic segmentation. To build a baseline for detection and segmentation tasks on TTPLA, we report the performance of several state-of-the-art deep learning models on our dataset. TTPLA dataset is publicly available at https://github.com/r3ab/ttpla_dataset

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Power Line Segmentation in Aerial Images Using Convolutional Neural Networks

Cited by 12 publications

References 11 publications

3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

Vision-Based UAV Distribution Line Inspection Using Deep Learning

TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines

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