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Efficient Light-Weight Deep Neural Network for Person Detection in Drone Images

Authors
Kim, M.Kim, H.Mok, Y.Paik, Joonki
Issue Date
Mar-2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Digest of Technical Papers - IEEE International Conference on Consumer Electronics, v.2022-January
Journal Title
Digest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume
2022-January
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56097
DOI
10.1109/ICCE53296.2022.9730191
ISSN
0747-668X
Abstract
In this paper, we propose an efficient light-weight deep neural network model for small object (person) detection in drone images. The proposed method performs light-weight as well as efficient small object detection by removing the head layers that detects large and medium-sized objects. In addition, the feature was extracted by focusing the weight on the small object while performing feature fusion through the Weighting Module. Finally, since the class imbalance problem between the object and the background is more serious in the drone image, the problem is alleviated by using the focal loss. As a result, the light-weight that can be mounted on the drone and the inference time are faster, and the Average Precision (AP) is higher than the original model. © 2022 IEEE.
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Paik, Joon Ki
첨단영상대학원 (영상학과)
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