An Indoor Pool Drowning Risk Detection Method Based on Improved YOLOv4

Niu, Qiang; Wang, Yucheng; Yuan, Songhe; Li, Kaixia; Wang, Xiaohu

doi:10.1109/imcec55388.2022.10020040

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The dataset comprises two distinct categories: drowning and non-drowning postures (Figure 3). While previous research primarily relied on fixed camera types such as wall-mounted cameras, overhead cameras, and underwater cameras for capturing imagery within pool areas or interiors [13,14], fixed cameras inherently suffer from limitations due to their rigid perspective, making them sensitive to variations in distance. This sensitivity leads to challenges such as target deformation and posture changes easily.…”

Section: Discussionmentioning

confidence: 99%

“…Drowning recognition has recently emerged as a significant area of application in machine learning. 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.…”

Section: Introductionmentioning

confidence: 99%

“…An enhanced Mask R-CNN algorithm [20] was introduced for timely drowning person detection within swimming pools, achieving a detection rate of 94.1% with a false detection rate of 5.9%. A novel drowning risk detection method based on YOLOv4 was proposed to establish an effective early-warning system [13], attaining a precision of 80.84% in identifying drowning categories. A video-based drowning detection system [21], rooted in background scene modeling, generated a large number of parameters, resulting in a red alert delay of 8.4 to 12.5 s from the onset of a crisis, wasting valuable rescue time.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%