|Table of Contents|

Improved SSD model in extraction application of expressway toll station locations from GaoFen 2 remote sensing image(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年02期
Page:
278-286
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Improved SSD model in extraction application of expressway toll station locations from GaoFen 2 remote sensing image
Author(s):
WANG Zheng-hong1;  YANG Chuan2
(1. China Highway Engineering Consulting Group Co., Ltd., Beijing 100089, China; 2. China Transport Telecommunications and Information Center, Beijing 100011, China)
Keywords:
traffic information;  deep learning;  object detection;  improved SSD algorithm;  high-resolution remote sensing;  toll station extraction
PACS:
U491.2
DOI:
10.19818/j.cnki.1671-1637.2021.02.024
Abstract:
The locations of expressway toll stations from GaoFen 2 remote sensing images were extracted as the research object. Expressway toll stations and 0.8 m remote sensing images of Beijing, Shanxi, Henan, Guangdong and Fujian in 2019 were selected to create a training sample dataset via image preprocessing, sample labeling, cropping, data enhancement, and sample dataset partition. Multiscale feature fusion was introduced to improve the target detection model of the single-shot multibox detector(SSD)by adding two operations, namely, “deconvolution” and “concat.” The semantic features of high-level feature maps were assigned to low-level feature maps to enhance the upsampling quality and feature fusion capabilities, thereby improved the detection performance on small targets toll stations. The improved SSD model was applied to extract the locations of toll stations in Fujian in 2019 from GaoFen 2 images. The images were automatically sliced along the Fujian highway network vectors, and the slices were input into the model for target detection. The slices with toll stations were retained, and non-maximum suppression was adopted to remove redundant detection frames. The coordinates of the remaining detection frames were transformed into the coordinates of the center points, and the center point vectors of the expressway toll stations were directly output. Thus, the automatic end-to-end extraction of toll station locations could be realized. Research results show that the accuracy and recall of the improved SSD model and their harmonic average are 0.86, 0.88, and 0.87, respectively, which are higher than those of the conventional SSD, VGG, Faster R-CNN, and Feature Pyramid Networks(FPN)models. Therefore, the proposed automatic extraction method for toll station locations can considerably improve management efficiency and adequately satisfy the actual needs of highway managers. 3 tabs, 7 figs, 35 refs.

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Last Update: 2021-06-01