Open AccessTarget detection algorithm based on improved multi-scale SSD
Author(s) -
Cao Jian-ying,
Yan Kong,
Xinlu Zhang,
Yongjia Li,
Xiaofeng Xie
Publication year - 2020
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 - 10.1088/1742-6596/1570/1/012014
Subject(s) - computer science , feature (linguistics) , feature extraction , scale (ratio) , pattern recognition (psychology) , artificial intelligence , dimension (graph theory) , algorithm , abstraction , layer (electronics) , mathematics , philosophy , linguistics , physics , chemistry , organic chemistry , epistemology , quantum mechanics , pure mathematics
The traditional SSD algorithm has a serious abstraction of feature extraction content, which makes it difficult to achieve effective detection of small targets. At the same time, the problem of feature layer fusion is difficult due to different scales. In this paper, an improved SSD based target detection algorithm is proposed. By introducing feature enhancement method, the adjustment steps of high-level feature size are omitted, Which makes it unnecessary to reduce the dimension of features, and at the same time, it uses the multi-scale candidate area which accords with the proportion of pedestrians in the detection network to enhance the feature extraction ability of small targets, effectively improves the accuracy and operation speed of SSD algorithm, and saves the loss of the network.