Simulated Photorealistic Deep Learning Framework and Workflows to Accelerate Computer Vision and Unmanned Aerial Vehicle Research.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021)(2021)
摘要
Deep learning (DL) is producing state-of-the-art results in a number of unmanned aerial vehicle (UAV) tasks from low level signal processing to object detection, 3D mapping, tracking, fusion, autonomy, control, and beyond. However, barriers exist. For example, most DL algorithms require big data, but supervised ground truth is a bottleneck, fueling topics like self-supervised learning. While it is well-known that hardware and data augmentation plays a significant role in performance, it is not well understood which data augmentations or what real data need be collected. Furthermore, existing datasets do not have sufficient ground truth nor variety to support adequate controlled experimental research into understanding and mitigating limitations in DL algorithms, models, data, and biases. In this article, we address the combination of photorealistic simulation, open source libraries, and high quality content (models, materials, and environments) to develop workflows to mitigate the above challenges and accelerate DL-enabled computer vision research. Herein, examples are provided relative to data collection, detection, passive ranging, and human-robot teaming. Online video tutorials are also provided at https://github.com/MizzouINDFUL/UEUAVSim.
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关键词
simulated photorealistic deep learning framework,workflows,accelerate computer vision,aerial vehicle research,unmanned aerial vehicle tasks,UAV,low level signal processing,DL algorithms,re-quire big data,supervised ground truth,bottle-neck,self-supervised learning,data augmentations,sufficient ground truth nor variety,adequate con-trolled experimental research,mitigating limitations,photo-realistic simulation,open source libraries,computer vision research,data collection
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