A video drowning detection device based on underwater computer vision

作     者：Liu, Tingzhuang He, Xinyu He, Linglu Yuan, Fei 

作者机构：Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informat Xiamen Peoples R China Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informat Xiamen 361005 Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET Image Proc.)

年 卷 期：2023年第17卷第6期

页      面：1905-1918页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Xiamen Ocean and fishery Development Special Fund project [21CZB015HJ10] 

主　　题：computer vision object detection real-time systems unsupervised learning 

摘      要：Drowning is a significant public health concern. A video drowning detection algorithm is a helpful tool for finding drowning victims. However, there are three challenges that drowning detection research typically encounters: a lack of actual drowning video data, subtle early drowning traits, and a lack of real time. In this paper, the authors propose an underwater computer vision based drowning detection device composed of embedded AI devices, camera, and waterproof case to solve the above problems. The detection device utilizes the high-performance computing of Jetson Nano to realize real-time detection of drowning events through the proposed drowning detection algorithm on the acquired underwater video stream. The proposed drowning detection algorithm primarily consists of two stages: in the first step, to successfully solve the interference of the surroundings and to give a trustworthy basis for video drowning detection, the YOLOv5n network is used to detect the near-vertical human body based on the characteristics of the drowning person. In the second stage, the authors propose a lightweight drowning detection network (DDN) based on a deep Gaussian model for fast feature vector detection. The lightweight DDN is combined with the Gaussian model to detect anomaly in the high-level semantic features, which has higher robustness and solves the lack of drowning videos. The experimental results show that the proposed drowning detection algorithm has good comprehensive performance and practical application value.