Abstract
Tunnel geological radar image has been widely used in tunnel engineering quality detection for its advantages of fast, nondestructive, continuous detection, real-time imaging, intuitive data processing and high detection accuracy. However, the traditional defect detection method, which is judged by surveyors visually, consumes energy. In order to detect the quality of tunnel engineering accurately and quickly, an improved method of void defect detection based on Faster RCNN (Regional Convolutional Neural Network) is proposed in depth learning. The image data of the tunnel geological radar is collected for annotation, which fills the blank of the defect data set in the tunnel engineering. Through the method of this paper proposed, the feature extraction is optimized to improve the performance of the detection model, and the detection accuracy of the model is verified by expert knowledge.