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EFLDet: enhanced feature learning for object detection

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Abstract

In this paper, we propose an enhanced feature learning method for object detection regarding with backbone, neck and head, which are three main components of object detection. For the backbone network, we build a bi-residual network to extract salient information by extending residual block with aggregate connection for the global feature representation. For the neck network, we design an enhanced feature pyramid network to fuse spatial and channel-wise information within different receptive fields of feature maps, which introduces the attention module with the global context block and the dilated convolution module to reduce the decay of information in the feature fusion. For the detection head, we construct a trident head network to improve the confidence of classification and regression, which consists of a fully connected head, a convolution head and an attention head. The experiments conducted on COCO dataset show that the proposed approaches are widely applicable and can verity the effectiveness for object detection.

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Acknowledgements

This work is supported by the Guangdong Basic and Applied Basic Research Foundation (No.2020A1515010616), Science and Technology Program of Guangzhou (No.202102020524, No.202007040005), the Guangdong Innovative Research Team Program (No.2014ZT05G157), the Key-Area Research and Development Program of Guangdong Province (2019B010136001), and the Science and Technology Planning Project of Guangdong Province (LZC0023).

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Yongwei Liao, Guipeng Zhang, Zhenguo Yang & Wenyin Liu

  2. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China

    Wenyin Liu

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  1. Yongwei Liao
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  2. Guipeng Zhang
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  3. Zhenguo Yang
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  4. Wenyin Liu
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Correspondence to Zhenguo Yang or Wenyin Liu.

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Liao, Y., Zhang, G., Yang, Z. et al. EFLDet: enhanced feature learning for object detection. Neural Comput & Applic 34, 1033–1045 (2022). https://doi.org/10.1007/s00521-021-06607-1

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