Open AccessA deep learning model S-Darknet suitable for small target detection
Author(s) -
Yingchun Miao,
Xun Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012118
Subject(s) - computer science , upsampling , feature (linguistics) , data mining , artificial intelligence , backbone network , pattern recognition (psychology) , machine learning , computer network , image (mathematics) , philosophy , linguistics
In order to solve the problem of low accuracy of small target detection in target detection, a small target detection model S-Darknet is proposed. The algorithm is designed based on the Darknet-53 network. First, a new backbone network is proposed, which fully extracts the feature of small objects and adapts to multi-scale detection. Then, in order to enhance the feature information of the target after the fusion, a feature enhancement module is added before each upsampling. Finally, the proposed algorithm was verified on the VOC2007, VOC2012 data sets and the actual data sets of railway freight locks. Experimental results show that this method has high detection accuracy under the premise of ensuring real-time performance.