A Water Behavior Dataset for an Image-Based Drowning Solution

Hasan, Saifeldin; Joy, John; Ahsan, Fardin; Khambaty, Huzaifa; Agarwal, Manan; Mounsef, Jinane

doi:10.1109/igessc53124.2021.9618700

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Li et al [19] obtain the final swimming pool intelligent-assisted drowning detection results through the YOLO principle. Hasan et al [39] propose a water behaviour dataset curated to support the design of image-based methods for drowning detection.…”

Section: Supervised Learning Vision-based Methodsmentioning

confidence: 99%

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A video drowning detection device based on underwater computer vision

Liu

et al. 2023

IET Image Processing

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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.

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Section: Supervised Learning Vision-based Methodsmentioning

confidence: 99%

“…Hasan et al. [39] propose a water behaviour dataset curated to support the design of image‐based methods for drowning detection.…”

Section: Related Workmentioning

confidence: 99%

A video drowning detection device based on underwater computer vision

Liu

et al. 2023

IET Image Processing

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“…Previous research predominantly relied on various cameras, including wall-mounted cameras, overhead cameras, and underwater cameras, for imagery capture within pool environments [13,14]. Hasan et al [15] introduced a water behavior dataset captured both above and below the water using cameras, comprising a water surface dataset and an underwater dataset for drowning detection. Analysis and experimentation conducted by Hasan et al [15] reveal that the performance of the water surface dataset is superior to that of the underwater dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Hasan et al [15] introduced a water behavior dataset captured both above and below the water using cameras, comprising a water surface dataset and an underwater dataset for drowning detection. Analysis and experimentation conducted by Hasan et al [15] reveal that the performance of the water surface dataset is superior to that of the underwater dataset. In the context of real-time water surface image analysis, expedited identification of drowning signs facilitates early detection, potentially saving valuable rescue time, minimizing submersion duration, and decreasing the number of drowning accidents.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5 Algorithm for Drowning Detection in the Indoor Swimming Pool

Yang,

Wang,

Yang

2023

Applied Sciences

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In order to mitigate the risk of irreversible drowning injuries, this study introduces an enhanced YOLOv5 algorithm aimed at improving the efficacy of indoor swimming pool drowning detection and facilitating the timely rescue of endangered individuals. To simulate drowning and swimming positions accurately, four swimmers were deliberately chosen and observed, with monitoring conducted by drones flying above the swimming pool. The study was approved by the ethics committee of our institution, with the registration number 2022024. The images captured by the drones underwent a meticulous evaluation, and only those deemed suitable were selected to construct the self-made dataset, comprising a total of 8572 images. Furthermore, two enhancements were implemented in the YOLOv5 algorithm. Firstly, the inclusion of the ICA module strengthened category classification and the localization of water behavioral postures, which is improved from the coordinated attention module (CA). Secondly, the PAN module was replaced with the bi-directional feature pyramid network (BiFPN). Subsequently, the improved YOLOv5 algorithm underwent training using the self-made dataset. Evaluation of the algorithm’s performance revealed a notably improved detection accuracy rate, recall rate, and an impressive mean Average Precision (mAP) score of 98.1%, 98.0%, and 98.5%, respectively. Our paper introduces the improved YOLOv5 algorithm, surpassing the original YOLOv5 algorithm in terms of recognition accuracy for instances of drowning.

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A video system based on convolutional autoencoder for drowning detection

Yuan

Liu

et al. 2023

Neural Comput & Applic

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A Water Behavior Dataset for an Image-Based Drowning Solution

Cited by 10 publications

References 8 publications

A video drowning detection device based on underwater computer vision

A video drowning detection device based on underwater computer vision

An Improved YOLOv5 Algorithm for Drowning Detection in the Indoor Swimming Pool

A video system based on convolutional autoencoder for drowning detection

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