Open AccessVehicle detection algorithm based on LW-SSD
Author(s) -
Yunxiang Liu,
Guoqing Zhang
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 - 10.1088/1742-6596/1748/3/032042
Subject(s) - computer science , softmax function , feature (linguistics) , porting , network model , residual , feature extraction , artificial intelligence , layer (electronics) , pooling , data mining , algorithm , pattern recognition (psychology) , deep learning , software , organic chemistry , philosophy , linguistics , chemistry , programming language
Due to the complex structure of the traditional SSD network model and the large amount of parameters, it is not suitable for porting to mobile or embedded platforms, so the LW-SSD vehicle detection algorithm is proposed, this paper redesigns the basic feature extraction network of SSD, using MobileNet feature extraction network instead of VGG16, for the original MobileNet to improve, remove the original MobileNet network average pooling layer and Softmax layer, while reducing the MobileNet network from the original 28 layers to 13 layers. Then, on the basis of the subsequent network, feature maps of different scales are scaled, residual mapping and deep feature fusion operations, and the low-level position information and high-level semantic information are effectively combined to obtain new feature maps of different scales. The designed network model is trained using the Tensorflow framework, and the test effect of the network model is evaluated. The experimental results show that the accuracy of the network model designed in this paper is 78.76%, which is 9.27% lower than the original SSD network model. However, the amount of parameters is only 1/4 of the SSD network model. At the expense of partial accuracy, the design of a lightweight network model is exchanged, which lays the foundation for subsequent development on mobile or embedded platforms.