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Feature Enhancement for Multi-scale Object Detection

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Abstract

Recently, deep learning has brought great progress in object detection. However, we believe that traditional hand-crafted features may still contain valuable human knowledge complementary to features learned from raw data. Besides, almost all top-performing object detection methods extract features by using backbones originally designed for image classification. The generated features are often highly semantic, which is beneficial to global image classification, but may lose details useful for object localization and recognition under various scales. To alleviate the problems mentioned above, a feature enhancement method is proposed in this paper. Inspired by the success of histograms of oriented gradients in traditional object detection research, we construct feature channels based on oriented gradients as input to convolutional neural networks to capture discriminative local orientations. The oriented gradients and RGB features are stacked as input of network to enhance the input feature representation. For accurate object localization and recognition, we employ dilated convolutions to increase spatial resolutions of output feature maps while maintaining their respective receptive fields. Hierarchical feature maps with different receptive fields are aggregated into the final feature representation for multi-scale object detection without extra upsampling. Experimental results on PASCAL VOC 2007 and 2012 demonstrate superiority of the proposed method compared with state-of-the-art methods for multi-scale object detection.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61172141, 61976231), Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011869), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase, No. U1501501), Project on the Integration of Industry, Education and Research of Guangdong Province (No. 2013B090500013), Science and Technology Program of Guangzhou (Nos. 201803030029, 2014J4100092), and Major Projects for the Innovation of Industry and Research of Guangzhou (No. 2014Y2-00213).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, 135 West Xingang Road, Guangzhou, 510275, China

    Huicheng Zheng, Lvran Chen, Ye Li & Zhiwei Yan

  2. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, 135 West Xingang Road, Guangzhou, 510275, China

    Huicheng Zheng, Lvran Chen, Ye Li & Zhiwei Yan

  3. Digital Grid Research Institute, China Southern Power Grid, 106 East Fengze Road, Guangzhou, 511458, China

    Jiajie Chen

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  1. Huicheng Zheng
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  2. Jiajie Chen
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  3. Lvran Chen
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  4. Ye Li
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Correspondence to Huicheng Zheng.

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Zheng, H., Chen, J., Chen, L. et al. Feature Enhancement for Multi-scale Object Detection. Neural Process Lett 51, 1907–1919 (2020). https://doi.org/10.1007/s11063-019-10182-x

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