A Study on Realtime Drone Object Detection Using On-board Deep Learning
Han-guk hanggong uju hakoeji/Han'gug hang'gong u'ju haghoeji(2021)
摘要
This paper provides a process for developing deep learning-based aerial object detection models that can run in realtime on onboard. To improve object detection performance, we pre-process and augment the training data in the training stage. In addition, we perform transfer learning and apply a weighted cross-entropy method to reduce the variations of detection performance for each class. To improve the inference speed, we have generated inference acceleration engines with quantization. Then, we analyze the real-time performance and detection performance on custom aerial image dataset to verify generalization.
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关键词
Deep Learning,Object Detection,Data Augmentation,Transfer Learning,Class Imbalance,Inference Acceleration
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