Leveraging Spatial Context Disparity for Power Line Detection

Pan, Chaofeng; Shan, Haotian; Cao, Xianbin; Li, Xuelong; Wu, Dapeng

doi:10.1007/s12559-017-9488-y

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…This is because metallic objects reflect most electromagnetic radiation in the visual spectrum [23]. Most of the auxiliaries for detecting power lines are manually determined as shown in [24]. However, a technique proposed in [25] to optimize the procedure uses a local optimization approach to automatically acquire the context information of each type of auxiliary.…”

Section: B Knowledge-based Methodsmentioning

confidence: 99%

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

et al. 2021

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Power outages can disrupt daily domestic activities as well as the economy as operations are hampered when they occur. They can decrease work productivity by delaying operations that require electricity. The key solution to this problem is to ensure that there are fewer or no power interruptions. This can be achieved by ensuring secure and continuous network operations through regular maintenance and inspection. However, the traditional inspection technique of foot patrol is risky, laborious, and timeconsuming. A preferable contemporary technique uses an unmanned aerial vehicle (UAV) for inspecting distribution lines. Detecting power lines are crucial for real-time motion planning and navigation of UAVs. Previous techniques that depend on conventional filters and gradients may fail to detect power lines because of noisy backgrounds. Thus, this study proposes a novel technique by adopting the Transfer Learning approach The process involves fine-tuning a pretrained network from Point Instance Network (PINet) road lane detection model to fit the power line detection task. The proposed method extends the PINet model by adding a comparator for rotation block before it and a postprocessing block after it. This study generates four versions of the model, each of which was trained on one of the following datasets (i) self-gathered images captured by a handheld camera, (ii) a drone, (iii) publicly accessible images from the Power Line Dataset of Mountain Scene (PLDM), and (iv) Power Line Dataset of Urban Scene (PLDU). Experimental results on each dataset confirm the feasibility of the proposed approach.

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Section: B Knowledge-based Methodsmentioning

confidence: 99%

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

et al. 2021

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“…To further improve auxiliaries-assisted power line detection accuracy and speed, Pan et al proposed a metric for measuring the usefulness of an auxiliary in assisting power line detection, named spacial context disparity, based on two factors: spatial context peakedness and spatial context difference and applied it for automatic selection of optimal auxiliaries [29]. According to the authors, the proposed method is robust and can achieve satisfactory performance for power line detection.…”

Section: Auxiliaries-assisted Methodsmentioning

confidence: 99%

LS-Net: fast single-shot line-segment detector

Nguyen

Jenssen

Roverso³

2020

Machine Vision and Applications

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In unmanned aerial vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach: (i) edge detection, (ii) the Hough transform, and (iii) spurious line elimination based on power line constrains. These approaches not only are slow and inaccurate but also require a huge amount of effort in post-processing to distinguish between power lines and spurious lines. In this paper, we introduce LS-Net, a fast single-shot line-segment detector, and apply it to power line detection. The LS-Net is by design fully convolutional, and it consists of three modules: (i) a fully convolutional feature extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the unavailability of large datasets with annotations of power lines, we render synthetic images of power lines using the physically based rendering approach and propose a series of effective data augmentation techniques to generate more training data. With a customized version of the VGG-16 network as the backbone, the proposed approach outperforms existing state-of-the-art approaches. In addition, the LS-Net can detect power lines in near real time. This suggests that our proposed approach has a promising role in automatic obstacle avoidance and as a valuable component of self-driving UAVs, especially for automatic autonomous power line inspection.

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“…To further improve auxiliaries assisted power line detection accuracy and speed, Pan et al proposed a metric for measuring the usefulness of an auxiliary in assisting power line detection, named spacial context disparity, based on two factors: spatial context peakedness and spatial context difference and applied it for automatic selection of optimal auxiliaries [28]. According to the authors, the proposed method is robust and can achieve satisfactory performance for power line detection.…”

Section: B Common Approaches To Power Line Detection 1) Line-based Me...mentioning

confidence: 99%

LS-Net: Fast Single-Shot Line-Segment Detector

Nguyen¹,

Jenssen²,

Roverso³

2019

Preprint

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In low-altitude Unmanned Aerial Vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach: (i) edge detection, (ii) the Hough transform, and (iii) spurious line elimination based on power line constrains. These approaches not only are slow and inaccurate but also require a huge amount of effort in postprocessing to distinguish between power lines and spurious lines. In this paper, we introduce LS-Net, a fast single-shot line-segment detector, and apply it to power line detection. The LS-Net is by design fully convolutional and consists of three modules: (i) a fully convolutional feature extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the unavailability of large datasets with annotations of power lines, we render synthetic images of power lines using the Physically Based Rendering (PBR) approach and propose a series of effective data augmentation techniques to generate more training data. With a customized version of the VGG-16 network as the backbone, the proposed approach outperforms existing state-of-the-art approaches. In addition, the LS-Net can detect power lines in near real-time (20.4 FPS). This suggests that our proposed approach has a promising role in automatic obstacle avoidance and as a valuable component of self-driving UAVs, especially for automatic autonomous power line inspection.

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Leveraging Spatial Context Disparity for Power Line Detection

Cited by 13 publications

References 39 publications

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

LS-Net: fast single-shot line-segment detector

LS-Net: Fast Single-Shot Line-Segment Detector

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