Reliable and Efficient Bear-presence Detection based on Region Proposal of Low-resolution

The bear attack to human beings is one of the fatal accidents, and it is becoming more critical to avoid such accidents because human ’s encountering a bear happens every year, even in a city area. It is required to discover bears quickly and warn people to avoid bear accidents. To realize sensor nodes that detect bears automatically using image recognition technology, we aim to realize an accurate and computationally-efficient bear-presence detection. In this paper, we propose a bear-presence detection method combining region proposal of a low-resolution and image classification. In the experiments, we show that the proposed method achieves 4.9% higher recall and 2.3% higher F-score than image classification with-out region-proposal. Moreover, the proposed method achieved 0.6% higher recall and 18.5% higher F-score than YOLOv3, which is one of state-of-the-art object detection methods while the execution time was reduced to 72.4% for bear images and 55.5% for non-bear images.