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
KEYWORDS: Electric fields, Point clouds, Distance measurement, LIDAR, Data acquisition, Tunable filters, Visual process modeling, Signal intensity, Visualization
With the rapid development of high-voltage transmission engineering, more and more cranes are operated around the high-voltage lines. Due to the high-altitude construction crane is too close to the high-voltage transmission lines, some accidents of the transmission lines often occur such as the short circuit and tripping, which has brought great impact on daily life and production. Therefore, it is necessary to design a system about anti-collision alarming against high-voltage transmission lines to prevent damages to operators. Considering the above situation, a system about anti-collision alarm of crane against high-voltage lines based on multi-modal information is proposed in this paper. The framework of this system is mainly divided into perception layer, detection layer and application layer. Through the simulation analysis and experimental verification of multimodal information, it indicates that our proposed system has a good effect both in real detection and anti-collision, which can effectively avoid the occurrence of accidents in the process of crane operation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.