Target Recognition Algorithm for Monitoring Images in Electric Power Construction Process
To enhance precision and comprehensiveness in identifying targets in electric power construction monitoring video, a novel target recognition algorithm utilizing infrared imaging is explored. This algorithm employs a color processing technique based on a local linear mapping method to effectively recolor monitoring images. The process involves three key steps: color space conversion, color transfer, and pseudo-color encoding. It is designed to accentuate targets in the infrared imaging. For the refined identification of these targets, the algorithm leverages a support vector machine approach, utilizing an optimal hyperplane to accurately predict target types. We demonstrate the efficacy of the algorithm, which achieves high target recognition accuracy in both outdoor and indoor electric power construction monitoring scenarios. It maintains a false recognition rate below 3% across various environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management
The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control …
Allenergy managementManagementMachine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
This review paper systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives. It is anticipated that with the rapid progress …
Domain AdaptationTransfer LearningTwo-Parameter CFAR Ship Detection Algorithm Based on Rayleigh Distribution in SAR Images
Synthetic Aperture Radar (SAR) is an active type of microwave remote sensing. Using the microwave imaging system, remote sensing monitoring of the land and global ocean can be done in any weather conditions around the cl…
2D Object DetectionImage SegmentationSAR Ship DetectionHigh-resolution power equipment recognition based on improved self-attention
The current trend of automating inspections at substations has sparked a surge in interest in the field of transformer image recognition. However, due to restrictions in the number of parameters in existing models, high-…
Region ProposalIMG-NILM: A Deep learning NILM approach using energy heatmaps
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load monitoring, NILM (Non-intrusive load moni…
Deep LearningNon-Intrusive Load MonitoringTime SeriesTime Series Analysis