Edge Detection and Feature Extraction Techniques in Image Processing of Dangerous Rock Bodies

In this article, edge detection and feature extraction technology in image processing of dangerous rock mass are studied. The purpose of this study is to propose an effective method to accurately extract the edges and features in the image of dangerous rock mass, and then provide technical support for monitoring and early warning of rock mass state. Therefore, this article adopts a method based on Convolutional Neural Network (CNN), and realizes the edge detection and feature extraction of dangerous rock image by designing an appropriate CNN model structure. The results show that the proposed method has significantly improved the recognition accuracy and computational efficiency compared with the traditional methods. The algorithm can automatically learn and extract the high-level features in the image of dangerous rock mass without complicated artificial feature engineering, and can meet the requirements of real-time monitoring in practical application. The edge detection and feature extraction technology of dangerous rock image based on CNN provides new ideas and means for dangerous rock monitoring, which is expected to play a greater role in practical application and provide strong support for ensuring people’s lives and property safety.