1. Equipment detection and recognition in electric power room based on faster R-CNN
- Author
-
Yan Li, Chang Xiaorun, Meng Zhaona, and Zhang Qianyi
- Subjects
Scheme (programming language) ,Training set ,Computer science ,business.industry ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Object detection ,Test (assessment) ,Set (abstract data type) ,Test set ,Electrical equipment ,0202 electrical engineering, electronic engineering, information engineering ,General Earth and Planetary Sciences ,020201 artificial intelligence & image processing ,Electric power ,Artificial intelligence ,business ,computer ,General Environmental Science ,computer.programming_language - Abstract
Faster RCNN is a popular and very efficient object detection algorithm, it has good performance in image recognition in various fields. In this paper, we applied the Faster RCNN algorithm to the equipment recognition and status detection tasks in the electric power room, then proposed a complete technical implementation scheme. First of all, after site investigation and shooting, we collected a total of 5600 pictures in 100 categories of electrical equipment. Then, we achieved the labeled pictures for all categories by employing an open source software named Labelimg. In the next step, the annotated data was randomly shuffled, 70% of which was used as the training set for model training, 10% of which was used as the validation set for model tuning, and the remaining data was used as the test set for model testing. We get less than 0.01% loss in training step and get 91.3% mAP for all test pictures in testing step. Besides, the test for video received a great performance. The well performance on the test pictures and videos showed the effectiveness and implementability of our technical solutions.
- Published
- 2021