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Region Proposal Approach for Human Detection on Aerial Imagery

机译:航空影像人体检测的区域提议方法

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In this paper we evaluated region proposal based CNN approach in human body detection from aerial perspective. Particular emphasis is on the automation of the detection for supporting search and rescue missions. This challenging task is characterized by two important requirements. The first requirement for proposed algorithm is real-time speed of execution and the other is exceptional detection quality on complex natural environment images. Evaluation is performed on high spatial resolution images with high level of details that were collected by UAV platforms. Evaluated method based on FasterRCNN detection model showed promising preliminary results, as well as fast processing of high resolution images. Overall, detection model achieved 88.3% recall with precision of 67.3%.
机译:在本文中,我们从空中角度评估了基于区域提议的CNN方法在人体检测中的应用。特别强调用于支持搜索和救援任务的自动检测。这项艰巨的任务有两个重要要求。提出算法的第一个要求是实时执行速度,另一个要求是在复杂的自然环境图像上具有出色的检测质量。对由UAV平台收集的具有高细节水平的高空间分辨率图像执行评估。基于FasterRCNN检测模型的评估方法显示出令人鼓舞的初步结果,以及对高分辨率图像的快速处理。总体而言,检测模型的召回率达到88.3%,准确率达到67.3%。

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