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

Wall bulge maintenance and repairing is an essential task for autonomous decoration robots. The problem of the wall bulge endpoints regression refers to identifying the position of the wall bulge endpoints in spatial coordinates. This problem is of significant importance for autonomous decoration robots as these robots target automatic maintenance and repairing of wall bulges, they must automatically recognize where to start and stop the repairing process. Training deep convolutional neural networks for supervised computer vision tasks requires a large number of annotated images. Since gathering annotated images for this task is difficult, laborious, and time-consuming, we proposed a model for detecting the wall bulge endpoints position based on deep transfer learning. Our proposed model is capable of classifying the wall bulge into one of four classes according to its orientation. Deep transfer learning transfers the knowledge acquired by deep learning models trained for a specific task and domain to another different but related task and domain. Our proposed model is mainly based on deep convolutional neural networks pre-trained on large datasets for tasks of object classification and detection. We transfer the knowledge acquired by the model from these tasks to solve both problems in our new task.

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“…The distance between the ground truth value (x 2 , y 2 ) and the predicted value (x ′ 2 , y ′ 2 ) of the end points is calculated using (6):…”

Section: A Proposed Accuracy and Performance Metricsmentioning

confidence: 99%

Deep Transfer Learning for Wall Bulge Endpoints Regression for Autonomous Decoration Robots

et al. 2022

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“…There are two datasets using synthetic PLs. In the first dataset [28], synthetic images of power lines are rendered using the physically based rendering approach to generate the training dataset. Synthetic PLs are randomly superimposed on 718k high dynamic range images collected from the internet.…”

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, as the vital components of power transmission to modern industry, is the key to ensuring the convenience and stability of social urban life [1]. With the increasing scale of infrastructure facilities for power transmission, there are more and more construction cranes around high-voltage power lines.…”

Section: Introductionmentioning

confidence: 99%

An instance segmentation approach for power lines detection based on image enhancement and deep neural networks

Luo,

Yin,

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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Power lines detection (PLD) plays a key role in ensuring the convenience and stability of social urban life. Deep learning-based methods have been widely investigated for power lines detection, which can prevent the operators from damage. However, due to the influence of light, angle, position and other factors, how to realize the reliable power line detection (PLD) automatically in real time and prevent accidents is a challenging problem. In this paper, a novel framework based yolcat is proposed to enhance the images and automatically detect power lines based on deep learning. The novel framework is by design fully convolutional, and it consists of two modules: (i) an image enhancement with Gaussian Pyramid, (ii) a line segment regressor. To evaluate the proposed methods, a public dataset is utilized to test the performance of line detection with standard metric of mean average precision (mAP). Results indicate that the proposed methods show the best performance of power line detection with the image enhancement across baseline methods

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

Cited by 3 publications

References 39 publications

Deep Transfer Learning for Wall Bulge Endpoints Regression for Autonomous Decoration Robots

Deep Transfer Learning for Wall Bulge Endpoints Regression for Autonomous Decoration Robots

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

An instance segmentation approach for power lines detection based on image enhancement and deep neural networks

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