Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (10): 198-206.doi: 10.11896/jsjkx.210800214

• Computer Graphics& Multimedia • Previous Articles     Next Articles

Object Detection Algorithm Based on Improved Split-attention Network

PAN Yi, WANG Li-ping   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2021-08-24 Revised:2022-03-04 Online:2022-10-15 Published:2022-10-13
  • About author:PAN Yi,born in 1996,postgraduate.His main research interests include object detection and multi-objective optimization.
    WANG Li-ping,born in 1964,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include computing intelligence and decision optimization.
  • Supported by:
    Key Technologies Research and Development Program of Zhejiang Province,China(2018C01080).

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Abstract: Recently,most object detection algorithms based on convolutional neural network have the problems of lacking of reasonable use of meaningful contextual information and are easy to miss the detection of hard targets.In order to solve these problems,this paper proposes an object detection algorithm based on improved split-attention networks.Firstly,the split attention mechanism is introduced,and the multi-path structure is combined with feature-map attention mechanism to improve its feature representations.Then,in the convolution layer,poly-scale convolution is used to replace the vanilla convolution to enhance the scale-sensitivity of the neural network.Finally,the proposed algorithm is applied to Faster R-CNN.Experiments are carried out on Pascal VOC and MS COCO datasets.Compared with the original algorithm,the mAP of the proposed algorithm has improved 1.6% and 2.4% respectively without introducing additional parameters and computational complexities,and the mAP of the proposed algorithm is also higher than that of other algorithms,which verifies its good performance.

Key words: Convolutional neural network, Contextual information, Object detection, Split-attention, Poly-scale convolution

CLC Number: 

  • TP391
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