Multispectral image fusion based pedestrian
detection using a multilayer fused deconvolutional
single-shot detector
Posted on 2020-04-21 - 15:05
Recent research has demonstrated that effective fusion of multispectral images (visible and thermal images)
enables robust pedestrian detection under various illumination conditions (e.g., daytime and nighttime). However,
there are some open problems such as poor performance in small-sized pedestrian detection and high
computational cost of multispectral information fusion. This paper proposes a multilayer fused deconvolutional
single-shot detector (MFDSSD) that contains a two-stream convolutional module (TCM) and a multilayer fused
deconvolutional module (MFDM). The TCM is used to extract convolutional features from multispectral input
images. Then, novel fusion blocks are incorporated into the MFDM to combine high-level features with rich
semantic information and low-level features with detailed information, aiming at generating features with strong
representational power for small pedestrian instances. In addition, we fuse multispectral information at multiple
deconvolutional layers in the MFDM via fusion blocks. This multilayer fusion strategy adaptively makes the most
use of visible and thermal information. In addition, using fusion blocks for multilayer fusion can reduce the extra
computational cost and redundant parameters. Empirical experiments show that the proposed approach achieves
81.82% average precision (AP) on a new small-sized multispectral pedestrian dataset. The proposed method
achieves the best performance on two well-known public multispectral datasets. For example, on the KAIST
multispectral pedestrian benchmark, our method achieves 97.36% AP and 20 fps detection speed, which
outperforms the state-of-the-art published method by 6.82% in AP and is three times faster in detection speed.
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Chen, Yunfan; shin, hyunchul (2020). Multispectral image fusion based pedestrian
detection using a multilayer fused deconvolutional
single-shot detector. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.4788342.v1
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