Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (04): 52-58.doi: 10.12052/gdutxb.200147

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A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion

Huang Jian-hang, Wang Zhen-you   

  1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2020-11-03 Online:2021-07-10 Published:2021-05-25

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Abstract: Through the study of feature levels in convolutional neural networks, this paper found that high-level feature have stronger semantic information and low resolution, and low-level features have strong resolution and weaker semantic information. Aiming at these problems, a object detection algorithm based on secondary feature fusion is proposed. The algorithm reuses transitional features and performs secondary feature fusion on the basis of Feature Pyramid Networks to supplement the rich low-level feature information to the top. Finally, the average accuracy of AP, AP50, and AP75 on the COCO2014 data set reach 35.3%, 57.5%, and 36.6%, respectively. Compared with the unused feature fusion method and the traditional feature fusion method, the average accuracy is increased by 2.4%, 3.7% and 2.4%, which significantly improves the missed detection and the detection of small targets.

Key words: feature fusion, object detection, convolutional neural network, feature reuse

CLC Number: 

  • TP242.6+2
[1] HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN [J]. IEEE transactions on pattern analysis & machine intelligence, 2020, 42(2): 386-397.
[2] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 779-788.
[3] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
[4] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu: IEEE, 2017: 6517-6525.
[5] LI Y, CHEN Y, WANG N, et al. Scale-aware trident networks for object detection[C]//IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 6053-6062.
[6] BHARAT S, MAHYAR N, LARRY S D. SNIPER: efficient multi-scale training[J]. arXiv preprint arXiv: 1805.09300, 2018.
[7] LIU W, ANUELOVG D, ERHAN D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Berlin: Springer, Cham, 2016: 21-37.
[8] KONG T, YAO A, CHEN Y, et al. Hypernet: Towards accurate region proposal generation and joint object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas: IEEE, 2016: 845-853.
[9] LIU S T, HUANG D, WANG Y H. Receptive field block net for accurate and fast object detection[J]. arXiv preprint arXiv: 1711.07767, 2017.
[10] LIN TY, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 2117-2125.
[11] LI Z, ZHOU F Q. FSSD: feature fusion single shot multibox detector[J]. arXiv preprint arXiv: 1712.00960, 2017.
[12] FU C Y, LIU W, RANGA A, et al. Dssd: deconvolutional single shot detector[J]. arXiv preprint arXiv: 1701.06659. 2017.
[13] 温捷文, 战萌伟, 李楚宏, 等. 一种加强SSD小目标检测能力的Atrous滤波器设计[J]. 计算机应用研究, 2019, 36(3): 861-865, 872.
WEN J W, ZHANM W, LI C H, et al. Design of Atrous filter to strengthen small object detection capability of SSD [J]. Application Research of Computers, 2019, 36(3): 861-865, 872.
[14] 高俊艳, 刘文印, 杨振国. 结合注意力与特征融合的目标跟踪[J]. 广东工业大学学报, 2019, 36(4): 18-23.
GAO J Y, LIU W Y, YANG Z G. Object tracking combined with attention and feature fusion [J]. Journal of Guangdong University of Technology, 2019, 36(4): 18-23.
[15] HE K, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas: IEEE, 2016: 770-778.
[16] REDMON J, FARHADI A. YOLOV3: an incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[17] ABADI M, AGARWAL A, BARHAM P, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems[J]. arXiv preprint arXiv: 1603.04467, 2016.
[18] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge [J]. International journal of computer vision, 2015, 115(3): 211-252.
[19] DAI J F, LI Y, HE K. R-FCN: object detection via region-based fully convolutional networks[J]. arXiv preprint arXiv: 1605.06409, 2016.
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