DrownACB-YOLO:an Improved YOLO for Drowning Detection in Swimming Pools

Abstract

With the rise in drowning accidents in swimming pools, the demand for the precision and speed in artificial intelligence(AI) drowning detection methods has become increasingly crucial. Here, an improved YOLO-based method, named DrownACB-YOLO, for drowning detection in swimming pools is proposed. Since existing methods focus on the drowned state, a transition label is added to the original dataset to provide timely alerts. Following this expanded dataset, two improvements are implemented in the original YOLOv5. Firstly, the spatial pyramid pooling(SPP) module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP) module and the content-aware reassembly of feature(CARAFE) module, respectively. Secondly, the cross stage partial bottleneck with three convolutions(C3) module at the end of the backbone is replaced with the bottleneck transformer(BotNet) module. The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.

Keywords

drowning detection / YOLO / atrous spatial pyramid pooling(ASPP) / content-aware reassembly of feature(CARAFE)

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