Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness

Bin ZHAO; Chunping WANG; Qiang FU

doi:10.11999/JEIT190761

Volume 42 Issue 10

Oct. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(10): 2524-2532

Bin ZHAO, Chunping WANG, Qiang FU. Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2524-2532. doi: 10.11999/JEIT190761

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Bin ZHAO, Chunping WANG, Qiang FU. Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2524-2532. doi: 10.11999/JEIT190761

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Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness

doi: 10.11999/JEIT190761

Department of Electronic and Optical Engineering, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China

Received Date: 2019-09-30
Rev Recd Date: 2020-05-13

Available Online: 2020-05-20

Publish Date: 2020-10-13

Abstract

Abstract

The infrared imaging system of Ultrawide Field Of View (U-FOV) has large monitoring range and is not limited by illumination, but there are diverse scales and abundant small objects. For accurately detecting them, a multi-scale infrared pedestrian detection method is proposed with the ability of background-awareness, which can improve the detection performance of small objects and reduce the redundant computation. Firstly, a four scales feature pyramid network is constructed to predict object independently and supplement detail features with higher resolution. Secondly, attention module is integrated into the horizontal connection of feature pyramid structure to generate salient features, suppress feature response of irrelevant areas and enhance the object features. Finally, the anchor mask generation subnetwork is constructed on the basis of salient coefficient to the location of the anchors, to eliminate the flat background, and to improve the processing efficiency. The experimental results show that the salient generation subnetwork only increases the processing time by 5.94%, and has the lightweight characteristic. The Average-Precision is 93.20% on the U-FOV infrared pedestrian dataset, 26.49% higher than that of YOLOv3. Anchor box constraint strategy can save 18.05% of processing time. The proposed method is lightweight and accurate, which is suitable for detecting multi-scale infrared objects in the U-FOV camera.
- Infrared pedestrian detection,
- Ultrawide Field Of View(U-FOV),
- Convolutional Neural Network(CNN),
- Background-awareness,
- Multi-scale

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