Construction of Intelligent Pole Climbing Behavior Detection and Alarm System Based on Moving Target Detection Algorithm

In the power industry, pole climbing is a common dangerous behavior, which may lead to serious safety accidents if it is not stopped. The purpose of this article is to study the application of moving target detection algorithm based on deep learning algorithm in pole climbing behavior detection and alarm. In this article, a moving target detection algorithm based on Generative Adversarial Network (GAN) is proposed, which improves the accuracy and real-time performance of target detection in video surveillance, and then realizes intelligent detection and alarm of pole climbing behavior. Finally, the feasibility and superiority of the optimized algorithm are verified by experiments. The results show that the model based on Deep Convolutional Neural Network (DCNN) and GAN can gradually converge in the training process, and the loss value of the generator is low, and the accuracy and recall rate are high, which is more than 20% higher than that of the behavior detection algorithm based on Support Vector Machine (SVM). The system realizes the function of target detection and real-time early warning, and has good real-time and robustness. The moving target detection algorithm based on DBN has a good application prospect in pole climbing behavior detection and alarm, and can provide a more accurate and real-time solution for the intelligent video surveillance field.