Development of outdoor swimmers detection system with small object detection method based on deep learning
Development of outdoor swimmers detection system with small object detection method based on deep learning
复制标题
DOI：
10.1007/s00530-022-00995-7
复制
发表时间：
2022-09
影响因子：
3.9
通讯作者：
Hanguang Xiao;Yuewei Li;Yu Xiu;Qingling Xia
中科院分区：
文献类型：
--
作者：
Hanguang Xiao;Yuewei Li;Yu Xiu;Qingling Xia
野外游泳，或在禁止的室外场所游泳，是溺水事件的主要来源，也是室外水上安全管理的重点问题。目前，人工巡逻和警示标志是当地政府户外水上安全管理检查溺水事故的基本方法。然而，它们效率低下，成本高昂，而且收效甚微。为了实现这一目标，利用微软公共对象上下文（MS COCO）数据集作为训练起点，通过迁移学习开发了一种新的户外游泳者对象检测器。然后对该模型进行评估和再训练，使其具备对游泳者、疑似游泳者和行人进行分类的能力。本文提出的基于小目标检测方法的游泳者检测的总精度和检测时间分别为99.45%和43.44 ms，高于现有方法和传统的数据增强方法。我们验证了所提方法在小目标检测上的有效性，并设计了两种硬件系统原型（固定监控装置和无人机监控装置），以满足固定和移动检测场景的要求，能够有效识别和预警可能出现的野生游泳现象。该方案可以为其他依赖摄像头的创新型城市应用提供更全面的参考，对社会有价值。
Wild swimming, or swimming in prohibited outdoor places, is a major source of drowning occurrences and a key problem in outdoor water safety management. Currently, manual patrol and warning signs are the basic methods adopted by the local government for outdoor water safety management to inspect drowning accidents. However, they are inefficient, costly, and of little avail. To this goal, a novel object detector for outdoor swimmers was developed via transfer learning utilizing the Microsoft Common Objects in Context (MS COCO) dataset as a training starting point. The model was then evaluated and retrained to possess the capacity to classify swimmers, suspected swimmers, and pedestrians. The total precision and detection time of our proposed swimmer detection with small object detection approach are 99.45% and 43.44 ms, respectively, which are greater than those of existing methods and traditional data augmentation methods. We verified the effectiveness of the proposed method on small target detection and designed two prototypes of hardware systems (fixed monitoring device and drone monitoring device) to meet the requirements of stationary and movable detection scenarios that can identify and warn of the possible phenomenon of wild swimming efficiently. This scheme can provide a more comprehensive reference for other innovative city applications that rely on cameras and can be valuable for society.