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|Abstract:||Existing object detection literature focuses on detecting a big object covering a large part of an image. The problem of detecting a small object covering a small part of an image is largely ignored. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. In this paper, we dedicate an effort to bridge the gap. We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. We then augment the state-of-the-art R-CNN algorithm with a context model and a small region proposal generator to improve the small object detection performance. We conduct extensive experimental validations for studying various design choices. Experiment results show that the augmented R-CNN algorithm improves the mean average precision by 29.8% over the original R-CNN algorithm on detecting small objects.|
|Citation:||Chen, Chenyi, Ming-Yu Liu, Oncel Tuzel, and Jianxiong Xiao. "R-CNN for Small Object Detection." In Asian Conference on Computer Vision (2016): pp. 214-230. doi:10.1007/978-3-319-54193-8_14|
|Pages:||214 - 230|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Asian Conference on Computer Vision|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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