Object detection in hospital facilities: A comprehensive dataset and performance evaluation
Object detection in hospital facilities: A comprehensive dataset and performance evaluation
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DOI：
10.1016/j.engappai.2023.106223
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Da Hu;Shuai Li;Mengjun Wang
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文献类型：
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作者：
Da Hu;Shuai Li;Mengjun Wang
检测医院室内环境中的物体对于场景理解至关重要，并且可以在医疗保健中有各种应用。深度学习算法已被证明在从图像或视频中识别物体方面是有效的，但注释数据集的可用性在其成功应用中起着至关重要的作用。然而，医院环境中物体检测的数据集短缺，阻碍了医院室内物体检测算法的发展。在本文中，我们提出了医院室内目标检测（HIOD）数据集，由4417张图像组成，涵盖56个目标类别。HIOD数据集表示医院中经常遇到的对象，包括51,869个注释对象。该数据集具有密集标注的特点，平均每幅图像有11.7个对象和6.8个对象类别。利用HIOD数据集和8个最先进的目标检测器建立了目标检测基准。该基准测试对所选对象检测器在医院环境中常见的大量不同对象图像集上的性能进行了全面评估。基准测试的结果可用于比较和分析不同对象检测器的性能，并确定它们在医院环境中使用的优缺点。在基准测试中，与相似参数大小的两级检测器相比，一级检测器表现出优越的性能。特别是，在255 FPS的检测速度下，YOLOv6-L能够达到51.7%的平均精度（mAP）。该基准和数据集可以作为计算机视觉和机器人领域的研究人员和从业人员的宝贵资源，有助于推动开发更有效和高效的目标检测算法，用于开发医院自动化操作，如机器人消毒和患者辅助。
Detecting objects in hospital indoor environments is critical for scene understanding and can have various applications in healthcare. Deep learning algorithms have proven to be effective in object recognition from images or videos, but the availability of annotated datasets plays a crucial role in their successful application. However, there is a shortage of datasets for object detection in hospital settings, hindering the advancement of hospital indoor object detection algorithms. In this paper, we present the Hospital Indoor Object Detection (HIOD) dataset, consisting of 4,417 images covering 56 object categories. The HIOD dataset represents the frequently encountered objects in hospitals and comprises 51,869 annotated objects. The dataset is characterized by dense annotation, with an average of 11.7 objects and 6.8 object categories per image. An object detection benchmark was established using the HIOD dataset and eight state-of-the-art object detectors. The benchmark provides a comprehensive evaluation of the performance of the selected object detectors on a large and diverse set of images of objects commonly seen in hospital environments. The results of the benchmark can be used to compare and analyze the performance of different object detectors and identify their strengths and weaknesses for use in hospital environments. In the benchmark, one-stage detectors have shown superior performance compared to two-stage detectors of similar parameter sizes. In particular, YOLOv6-L was able to attain a mean Average Precision (mAP) of 51.7% while operating at a detection speed of 255 FPS. The benchmark and dataset can serve as a valuable resource for researchers and practitioners in the field of computer vision and robotics, helping to advance the development of more effective and efficient object detection algorithms for developing automated operations in hospitals such as robotic disinfection and patient assistance.