To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A$^2$-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel pplication-Aware Task Planning Problem (A$^2$-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A$^2$-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A$^2$-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet dataset. Results show that A$^2$-UAV attains on average around 38% more accomplished tasks than the state-of-the-art, with 400% more accomplished tasks when the number of targets increases significantly. To allow full reproducibility, we pledge to share datasets and code with the research community.

翻译：为执行高级监控任务，无人飞行器（UAV）需运行边缘辅助的计算机视觉（CV）任务。在多跳无人机网络中，由于带宽严重受限，这些任务向边缘的成功传输面临严峻挑战。为此，我们提出一种新型A$^2$-UAV框架，用于优化边缘端正确执行的任务数量。与现有技术截然不同，我们采用面向应用的方法，并制定了一个新颖的面向应用的任务规划问题（A$^2$-TPP）。该问题综合考虑了以下因素：（i）基于可用数据集，针对感兴趣类别，深度神经网络（DNN）准确率与图像压缩之间的关系；（ii）目标位置；（iii）无人机的当前能量/位置，以优化每架无人机的路由、数据预处理与目标分配。我们证明A$^2$-TPP是NP-难问题，并提出一种多项式时间算法高效求解。通过由四架DJI Mavic Air 2无人机组成的测试平台进行实际实验，我们对A$^2$-UAV进行了全面评估。我们考虑了四种不同DNN模型（即DenseNet、ResNet152、ResNet50和MobileNet-V2）的最新图像分类任务，以及使用基于ImageNet数据集训练的YoloV4进行的目标检测任务。结果表明，A$^2$-UAV平均完成的正确任务数量比现有技术高出约38%，当目标数量显著增加时，完成的正确任务数量比现有技术高出400%。为保证完全可复现，我们承诺与研究社区共享数据集和代码。