Abstract Research and development in mobile robotics are continuously growing. The ability of a human-made machine to navigate safely in a given environment is a challenging task. In agricultural environments, robot navigation can achieve high levels of complexity due to the harsh conditions that they present. Thus, the presence of a reliable map where the robot can localize itself is crucial, and feature extraction becomes a vital step of the navigation process. In this work, the feature extraction issue in the vineyard context is solved using Deep Learning to detect high-level features – the vine trunks. An experimental performance benchmark between two devices is performed: NVIDIA’s Jetson Nano and Google’s USB Accelerator. Several models were retrained and deployed on both devices, using a Transfer Learning approach. Specifically, MobileNets, Inception, and lite version of You Only Look Once are used to detect vine trunks in real-time. The models were retrained in a built in–house dataset, that is publicly available. The training dataset contains approximately 1600 annotated vine trunks in 336 different images. Results show that NVIDIA’s Jetson Nano provides compatibility with a wider variety of Deep Learning architectures, while Google’s USB Accelerator is limited to a unique family of architectures to perform object detection. On the other hand, the Google device showed an overall Average precision higher than Jetson Nano, with a better runtime performance. The best result obtained in this work was an average precision of 52.98% with a runtime performance of 23.14 ms per image, for MobileNet-V2. Recent experiments showed that the detectors are suitable for the use in the Localization and Mapping context.

中文翻译：

---

使用深度学习检测葡萄园树干——实验设备基准

摘要 移动机器人的研究和发展不断增长。人造机器在给定环境中安全导航的能力是一项具有挑战性的任务。在农业环境中，机器人导航可能会由于其呈现的恶劣条件而实现高度的复杂性。因此，机器人可以定位自己的可靠地图的存在至关重要，特征提取成为导航过程的重要步骤。在这项工作中，葡萄园环境中的特征提取问题使用深度学习检测高级特征——葡萄树干来解决。在两个设备之间执行实验性能基准测试：NVIDIA 的 Jetson Nano 和 Google 的 USB Accelerator。使用迁移学习方法在两种设备上重新训练和部署了几个模型。具体来说，MobileNets，Inception 和精简版 You Only Look Once 用于实时检测葡萄树干。这些模型在公开可用的内置内部数据集中进行了重新训练。训练数据集包含 336 个不同图像中的大约 1600 个带注释的葡萄树干。结果表明，NVIDIA 的 Jetson Nano 提供与更广泛的深度学习架构的兼容性，而 Google 的 USB Accelerator 仅限于执行对象检测的独特架构系列。另一方面，谷歌设备显示出比 Jetson Nano 更高的整体平均精度，具有更好的运行时性能。在这项工作中获得的最佳结果是 MobileNet-V2 的平均精度为 52.98%，每张图像的运行时性能为 23.14 毫秒。