Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets basically lack the test set annotations, causing researchers must compare their method with other SOTAs on a self-divided test set (from the training set). Training other methods lead to an increase in workload and different researchers divide different datasets, resulting there is no unified benchmark to compare the performance of different algorithms. Secondly, these datasets also have other shortcomings, e.g., too many similar images or incomplete labels. Towards these challenges we introduce a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark, based on the collection and re-annotation of all relevant datasets. DUO contains a collection of diverse underwater images with more rational annotations. The corresponding benchmark provides indicators of both efficiency and accuracy of SOTAs (under the MMDtection framework) for academic research and industrial applications, where JETSON AGX XAVIER is used to assess detector speed to simulate the robot-embedded environment.
翻译:用于机器人采摘的水下物体的探测引起了很大的兴趣。然而,由于若干挑战,这仍然是一个尚未解决的问题。我们采取了步骤,通过应对以下挑战,使数据更加现实。首先,现有数据集基本上缺乏测试数据集的注释,研究人员必须在一个自分类的测试数据集(从培训数据集)上将其方法与其他SOTA方法进行比较。培训其他方法导致工作量增加,不同的研究人员将不同的数据集区分开来,从而没有统一的基准来比较不同算法的性能。第二,这些数据集还存在其他缺点,例如,类似图像过多或标签不完整。为了应对这些挑战,我们引入了一个数据集,检测水下物体(DUO),并根据所有相关数据集的收集和重新说明,将自己的方法与其他SOTA方法进行比较。 DUO收集了多种水下图像,并有更合理的说明。相应的基准为学术研究和工业应用提供了效率和准确性指标(MMDDTection框架),例如,过多的类似图像或不完整的标签。为了应对这些挑战,我们引入了一套数据集,即检测所有相关数据集(DUEVIVI)和相应的基准,用以评估检测机器人环境的探测速度。