Electronic Science and Technology ›› 2024, Vol. 37 ›› Issue (2): 14-22.doi: 10.16180/j.cnki.issn1007-7820.2024.02.003

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Small Object Detection Based on Convolution and Self-Attention of Aggregation

WANG Xiaozhu,YU Lianzhi   

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093,China
  • Received:2022-09-27 Online:2024-02-15 Published:2024-01-18
  • Supported by:
    National Natural Science Foundation ofChina(61603257)


Small object detection is a research hotspot in most object detection open datasets. In view of the problem of insufficient detection accuracy of small targets in multi-size detection scenarios, an improved small target detection model based on YOLOv5s(You Only Look Once version 5s) is proposed in this study.A convolution self-attention aggregation residual block is added to the feature extraction network of the detector to improve the feature extraction ability, and a new feature graph is introduced from the shallow network to enhance the feature information of small object. The feature fusion network structure is improved to make full use of the newly introduced shallow features. SIOU Loss is introduced to replace the original GIOU Loss rectangular frame loss function to improve the detection accuracy and training speed.The experimental results show that the detection accuracy of the improved model is 0.012 higher than YOLOv5s on the 2007 and 2012 data sets of PASCAL VOC, and the small object detection accuracy is 0.023 higher than YOLOv5s. The detection accuracy of the imporved model in MS COCO data set is 0.001 higher than YOLOv5s, and the detection accuracy of small objects is 0.009 higher than YOLOv5s.

Key words: small object, object detection, YOLOv5s, convolutional neural network, self-attention, ACmix, SIOU Loss, residual network

CLC Number: 

  • TN247