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
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