Dangerous Behavior Detection Based on Convolutional Neural Network Algorithm

Behavior detection is an important research direction in the field of deep learning. CNN has achieved great success in the field of image classification, which promotes the research and development in the field of behavior detection. This paper studies dangerous behavior detection based on CNN algorithm. First of all, the problems of traditional risky behavior detection algorithms are studied, as well as the research progress and development trend of risky behavior detection based on CNN. Then, we study the basis of CNN algorithm, construct CNN network model, and study the working process of convolutional layer, pooling layer and fully connected layer. Then, we study CNN algorithm optimization, including Softmax regression optimization and back propagation algorithm optimization. Finally, KTH data set was selected for simulation experiment, and parameters such as learning rate, convolution kernel and Batch Size were scheduled. On this basis, comprehensive test of risky behavior was carried out. The results show that the CNN algorithm optimized in this paper can improve the accuracy of behavior recognition, which is of great significance in laboratory safety and other fields.