Real-time debris flow detection using deep convolutional neural network and Jetson Nano

Department of Ocean Engineering, Pukyong National University, Busan 48513, Korea

^* Corresponding author: yuntkim@pknu.ac.kr

Abstract

This study aims to develop a potential system for real-time detection of debris flow motion using a deep convolutional neural network (CNN) and image processing techniques. A system consisting of a pre-trained CNN model, NVIDIA Jetson Nano, and a camera was used to identify debris flow movement. The pre-trained CNN model was trained on an image dataset derived from 12 debris flow videos obtained from small flume tests, large flume tests, and several debris flow events. The application results of the proposed system on the flume test in the laboratory reached an F1 score of 72.6 to 100%. The real-time processing speed of the CNN model achieved from 2 to 21 frames per second (FPS) on the Jetson Nano. Both the accuracy and the processing speed of CNN model depend on the size of the video input and the input size of the model CNN. The CNN model of 320 × 320 pixels with a resolution of 800 × 480 pixels gives accuracy (F1 = 99.2%) and processing speed (FPS = 20) considered the optimal model when running the Jetson Nano device; thus, it can be applied for early detection and warning systems.