Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable performance. Unfortunately, training such systems requires several hours of GPU time. For robots, to successfully adapt to changes in the environment or learning new objects, it is also important that object detectors can be re-trained in a short amount of time. A recent method [1] proposes an architecture that leverages on the powerful representation of deep learning descriptors, while permitting fast adaptation time. Leveraging on the natural decomposition of the task in (i) regions candidate generation, (ii) feature extraction and (iii) regions classification, this method performs fast adaptation of the detector, by only re-training the classification layer. This shortens training time while maintaining state-of-the-art performance. In this paper, we firstly demonstrate that a further boost in accuracy can be obtained by adapting, in addition, the regions candidate generation on the task at hand. Secondly, we extend the object detection system presented in [1] with the proposed fast learning approach, showing experimental evidence on the improvement provided in terms of speed and accuracy on two different robotics datasets. The code to reproduce the experiments is publicly available on GitHub.
翻译:检测物体是机器人在非结构化环境中操作的基本任务。 今天, 有几个深层次的学习算法可以出色地完成这项任务。 不幸的是, 培训这些系统需要几小时的GPU时间。 对于机器人来说, 要成功地适应环境变化或学习新对象, 还必须在短短的时间内重新训练物体探测器。 最近的方法[ 1 提议了一个架构, 利用深层学习描述器的强大代表, 同时允许快速适应时间。 在( 一) 区域候选生成, (二) 特征提取和(三) 区域分类中, 利用自然分解任务的方法, 仅通过对分类层进行再培训, 对探测器进行快速调整。 这缩短了培训时间,同时保持最先进的性能。 在本文中, 我们首先证明, 可以通过调整手头任务的区域候选生成来进一步提高准确性。 其次, 我们将Sow中显示的天体探测系统与拟议的快速学习方法一起扩展, 显示以速度和精确度改进方式提供的天体探测器的实验证据, 在两种不同的机器人复制器上, 。