Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (5): 7-13.doi: 10.16180/j.cnki.issn1007-7820.2022.05.002

Previous Articles     Next Articles

Research on Supervision Object Detection Based on Improved SSD

HUANG Jing,XIE Xuan   

  1. School of Information Science and Technology,Zhejiang University of Science and Technology, Hangzhou 310018,China
  • Received:2020-12-31 Online:2022-05-25 Published:2022-05-27
  • Supported by:
    Zhejiang Key R&D Program(2021C01048)


In view of many problems caused by manual acceptance in decoration projects, this study proposes an improved SSD algorithm and applies it to supervision work to replace manual acceptance and promote the realization of intelligent supervision. Because the SSD algorithm has problems such as rechecking the same target and poor detection of small targets, the DPN network is employed to replace the basic feature extraction network VGG16. DPN combines the advantages of Resnet and Densenet, and has better feature extraction capabilities. Feature maps are fused by weighted FPN to highlight the contributions of feature maps of different layers and enrich the semantics of feature maps for prediction. Using depth separable convolution can reduce the amount of model parameters and improve the inference speed of the algorithm. Experimental comparison shows that the average accuracy of the improved model is increased by 3.47%, and the average accuracy of small numbers of detection is increased by up to 15%, which proves that the new model is effective in the task of supervision target detection.

Key words: supervision, SSD, VGG16, DPN, Resnet, Densenet, depth separable convolution, FPN

CLC Number: 

  • TP311.1