Channel-wise Adaptive Feature-level Fusion
| Autor: | Lin, Yang-Jiun, 林暘竣 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Object detection has been a hot research topic for many years to date. Among various tasks which we have achieved so far, recent state-of-the-art object detection through deep learning architecture methods have proven to be very robust to many kinds of image scenario. However, in reality, sensor noise like lighting changes or motion blur might confuse our system to make wrong decisions. To tackle these challenges, many methods based on sensor fusion have been proposed. Among them, Mees et al. [1] proposed an adaptive decision fusion approach for object detection that learns weighting the predictions of different sensor modalities. They achieved excellent performance that made system select better sensor in varied environment. However, they have to compute weights for each proposal which may cost more time, and it is unreasonable to apply the approach to other object detection methods such as SSD [2] and RFCN [3] because they save the time which applying a costly per-region subnetwork hundreds of times. In this paper, we aim to learn weighting the channels of intermediate feature maps instead of weighting final predictions of different sensor so that we don’t need to compute weights for each proposal. Compared with normal feature-level fusion methods, our method can also handle the problem of high sensor noise. We test our methods on InOutDoor RGBD People Dataset created by Mees et al. and demonstrated that our method is workable. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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