Application Of Image Recongnition Based On Convolutional Neural Network In Safety Helmet Recognition In Power Distribution Room

With the continuous development of power system production automation,more and more power distribution rooms are gradually adopting the unattended operation mode,using digital image processing technology to analyze and identify the monitoring images,which greatly improves the safety and reliability of unattended power distribution room.This paper uses deep learning technology to achieve intelligent recognition of the unattended distribution room monitoring images.Deep learning without manual operation is to original data features through a multi-step feature conversion to obtain a feature representation,and further input it into the prediction function to obtain the final result.Convolutional neural network is the most efficient image recognition technology in the deep learning model,which has a good robustness to identify the objects with rotation,translation,scaling.Based on deep theoretical research of CNNs,this paper applies it to safety helmet wearing and color recognition in power distribution rooms.Firstly,this paper introduces the development history of CNNs and related basic theoretical knowledge.It provides a theoretical basis for the research of neural network-based object recognition algorithms.Secondly,there is no ready-made safety helmet wearing and color recognition database.This paper completes the construction of the database through a series of work such as image data collection,data annotation,data classification.Then introduc-ed several data expansion methods and principles and further improved the database through these methods.Then,in order to solve the shortcomings of the traditional machine learning that manually extract features,this paper proposes to use CNN to automatically extract image features and expounds the YOLOv3 algorithm that abstracts object detection into a simple regression problemFinally,by comparing the YOLOv3 algorithm after data expansion,the YOLOv3 algorithm after transfer learning,and the YOLOv3 algorithm of the initial database,it is verified that data expansion and transfer learning have great effect on the improvement of algorithm performance.Then,the YOLOv3 algorithm is optimized by modifying the full connection layer and anchor box,and the performance of its safety helmet wearing and color recognition is compared and analyzed with the original YOLOv3 algorithm.It is found that the optimized YOLOv3 has better recognition effect on the wearing and color recognition of safety helmet in the distribution roomThe experiment proves that the optimized YOLOv3 algorithm in this paper basically meets the requirements of the helmet wearing and color recognition performance of the operation and maintenance operators in the power distribution room,laying a foundation for the subsequent application of the helmet wearing and color recognition technology to the intelligent video surveillance system in the power distribution room,Also provides a new idea for the identification method of the helmet wearing status of the power distribution room.The experiments show that the optimized YOLOv3 algorithm suffices the requir-ements of safety helmet wearing and color recognition performance of operation and maintenance workers in power distribution room.It lays a foundation for the application of safety helmet wearing and color recognition technology in intelligent video monitor system of power distribution room,and also provides a new idea for the condition identification method of safety helmet wearing in distribution room.