论文标题
深度学习的计算机视觉算法用于实时无人机在板载摄像机图像处理
Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing
论文作者
论文摘要
本文介绍了如何应用基于深度学习的计算机视觉算法来实现小型无人机的实时板载传感器处理。考虑了四种用例：目标检测，分类和本地化，用于GNSS贬低区域的自动导航的道路分割，人体分割和人体行动识别。所有算法都是使用基于深神经网络的最新图像处理方法开发的。已经开展了收购活动，以收集反映典型操作方案的自定义数据集，其中复制了多旋转无人机的特殊观点。报告了算法体系结构和训练有素的模型性能，显示出很高的准确性和推理速度。提供了输出示例和现场视频，展示了在GPU驱动的商业嵌入式设备（NVIDIA JETSON XAVIER）上部署在自定义四轮摩托车板上的模型操作，从而为高级自主权铺平了道路。
This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. Four use cases are considered: target detection, classification and localization, road segmentation for autonomous navigation in GNSS-denied zones, human body segmentation, and human action recognition. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks. Acquisition campaigns have been carried out to collect custom datasets reflecting typical operational scenarios, where the peculiar point of view of a multi-rotor UAV is replicated. Algorithms architectures and trained models performances are reported, showing high levels of both accuracy and inference speed. Output examples and on-field videos are presented, demonstrating models operation when deployed on a GPU-powered commercial embedded device (NVIDIA Jetson Xavier) mounted on board of a custom quad-rotor, paving the way to enabling high level autonomy.
