A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images
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TLDR
This paper constructs a power line data set using UAV images and classify the data according to the image clutter and proposes a method combining line detection and semantic segmentation, which shows better performance on images with different IC.Abstract:
Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection.read more
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References
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LSD: A Fast Line Segment Detector with a False Detection Control
TL;DR: A linear-time line segment detector that gives accurate results, a controlled number of false detections, and requires no parameter tuning is proposed.
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Markov Random Field Modeling in Computer Vision
TL;DR: This book presents a comprehensive study on the use of MRFs for solving computer vision problems, and covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms.
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Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle
TL;DR: The ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors is demonstrated, demonstrating comparable estimations, if not better, than those obtained by traditional manned airborne sensors.
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Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
TL;DR: A new automatic autonomous vision-based power line inspection concept is proposed that uses Unmanned Aerial Vehicle (UAV) inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of data analysis and inspection.
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Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform
TL;DR: In this paper, a pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines, which is used by performing knowledge-based line clustering in Hough space to refine the detection results.