It is important to accurately detect steel plants for capacity reduction monitoring and environmental protection. The traditional method is time-consuming and laborious, and can not be used to monitor the steel plants in large areas. We propose a stable and accurate method by adding a maxout module to SSD, namely, transforming the negative sample path into a multi-branch structure. The neural network learns abundant features of hard negative samples, and thereby increases resistance to the useless features. Meanwhile, we used the well-trained model to detect steel plants in the Jing-Jin-Ji area based on GF-1 data. The results were compared with the data of the steel plants obtained from visual interpretation. Our method detects steel plants in the Jing-Jin-Ji area with an accuracy of more than 80%.
LU Kaixuan
,
LI Guoqing
,
CHEN Zhengchao
,
ZAN Luyang
,
LI Baipeng
,
GAO Jianwei
. Extraction of steel plants based on optimized SSD network incorporating negative sample's multi channels[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(3)
: 352
-359
.
DOI: 10.7523/j.issn.2095-6134.2020.03.008
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