论文标题
对潜水员检测的深对象检测器的分析
An Analysis of Deep Object Detectors For Diver Detection
论文作者
论文摘要
为了选择和使用潜水员检测模型来支持人类机器人协作功能，例如潜水员跟随，我们彻底分析了一大批深神经网络以进行潜水员检测。首先，我们制作了一个数据集，该数据集来自来自视频的大约105,000张带注释的潜水员图像，这是有史以来最大，最多样化的潜水员检测数据集之一。使用此数据集，我们训练各种最先进的深神经网络进行对象检测，包括具有Mobilenet，更快的R-CNN和Yolo的SSD。与这些单帧检测器一起，我们还使用时间信息以及单帧图像信息来训练旨在检测视频流中对象的网络。我们根据典型的准确性和效率指标以及检测的时间稳定性评估了这些网络。最后，我们分析了这些探测器的故障，指出了最常见的失败情况。根据我们的结果，我们建议SSD或Tiny-Yolov4用于机器人实时应用，并建议对视频对象检测方法进行进一步研究。
With the end goal of selecting and using diver detection models to support human-robot collaboration capabilities such as diver following, we thoroughly analyze a large set of deep neural networks for diver detection. We begin by producing a dataset of approximately 105,000 annotated images of divers sourced from videos -- one of the largest and most varied diver detection datasets ever created. Using this dataset, we train a variety of state-of-the-art deep neural networks for object detection, including SSD with Mobilenet, Faster R-CNN, and YOLO. Along with these single-frame detectors, we also train networks designed for detection of objects in a video stream, using temporal information as well as single-frame image information. We evaluate these networks on typical accuracy and efficiency metrics, as well as on the temporal stability of their detections. Finally, we analyze the failures of these detectors, pointing out the most common scenarios of failure. Based on our results, we recommend SSDs or Tiny-YOLOv4 for real-time applications on robots and recommend further investigation of video object detection methods.
