4.7 Article

FastAER Det: Fast Aerial Embedded Real-Time Detection

期刊

REMOTE SENSING
卷 13, 期 16, 页码 -

出版社

MDPI
DOI: 10.3390/rs13163088

关键词

aerial object detection; deep learning based detection; embedded platforms; runtime optimization

资金

  1. Fraunhofer-Gesellschaft
  2. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB

向作者/读者索取更多资源

The article discusses the task of designing an on-board aerial object detector and presents a design process. By optimizing the baseline model and proposing a fast detection head, significant improvement in runtime is achieved while maintaining accuracy. In addition, several aspects to consider during deployment and in the runtime environment are discussed.
Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community.

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