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MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

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Abstract

With pedestrian detection algorithms, balancing the trade-off between accuracy and speed remains challenging. Following the central point-based one-stage object detection paradigm, a pedestrian detection algorithm based on multi-scale attention feature aggregation (MAFA) is proposed to improve accuracy while considering real-time performance. We refer to the proposed algorithm as MAFA-Net. Through the design of deep dilate blocks, deeper features are extracted. Pedestrian attention blocks are added to mine more relevant information between features from the perspective of spatial and passage-wise dimensions, and pedestrian features are enhanced. Feature aggregation modules are used to fuse different scale features, and combine the rich high-level semantic features with the accurate location features of the low-level features. Experiments were conducted on two challenging pedestrian detection datasets, i.e., CityPersons and Caltech, using MR−2 as the evaluation index. For Caltech, MR−2 is 4.58% under reasonable conditions. For CityPersons, MR−2 is 11.47% and 10.05% under reasonable and partial occlusion conditions, which is 0.43% and 1.35% better than the suboptimal comparison detection method. The results demonstrate that a good performance is obtained, and the effectiveness and feasibility of the algorithm are verified.

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Acknowledgments

This study was sponsored by the China Shandong Key R&D Plan (2018GGX106008) and was supported by the China Shandong Key Laboratory of Medical Physical Image Processing Technology.

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Authors and Affiliations

  1. School of Physics and Electronic Science, Shandong Normal University, Jinan, 250358, China

    Hao Xia, Honglin Wan, Jiayu Ou, Jun Ma, Xinyao Lv & Chengjie Bai

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  1. Hao Xia
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  2. Honglin Wan
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  3. Jiayu Ou
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  4. Jun Ma
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  5. Xinyao Lv
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  6. Chengjie Bai
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Correspondence to Hao Xia or Chengjie Bai.

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Xia, H., Wan, H., Ou, J. et al. MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation. Appl Intell 52, 7686–7699 (2022). https://doi.org/10.1007/s10489-021-02796-3

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