TY - GEN
T1 - Research on Target Detection Based on Deep Learning
AU - Liao, Pengjun
AU - Xu, Jinxiang
AU - Guo, Shangkun
AU - Qu, Jingkun
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In modern war, the environment is complex and changeable, so how to detect and attack targets automatically and effectively is of great significance. In this paper, through the analysis of the characteristics of targets under complicated environment, six targets, the aeroplanes, bridges, vehicles, ships, submarines and tanks are selected as the objects to be detected, and a large number of corresponding images of them are collected via Internet, and with reference to the dataset format of PASCAL VOC [1], the collected six types of target images are manually annotated to set up the dataset. Then, a corresponding detection model which will be used to detect the six types of targets is built. Base on the dataset built earlier, the detection model is trained and improved by modify its anchors properly.
AB - In modern war, the environment is complex and changeable, so how to detect and attack targets automatically and effectively is of great significance. In this paper, through the analysis of the characteristics of targets under complicated environment, six targets, the aeroplanes, bridges, vehicles, ships, submarines and tanks are selected as the objects to be detected, and a large number of corresponding images of them are collected via Internet, and with reference to the dataset format of PASCAL VOC [1], the collected six types of target images are manually annotated to set up the dataset. Then, a corresponding detection model which will be used to detect the six types of targets is built. Base on the dataset built earlier, the detection model is trained and improved by modify its anchors properly.
KW - Convolutional neural network
KW - Deep learning
KW - Target detection
UR - http://www.scopus.com/inward/record.url?scp=85130883308&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-9492-9_81
DO - 10.1007/978-981-16-9492-9_81
M3 - Conference contribution
AN - SCOPUS:85130883308
SN - 9789811694912
T3 - Lecture Notes in Electrical Engineering
SP - 820
EP - 829
BT - Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021
A2 - Wu, Meiping
A2 - Niu, Yifeng
A2 - Gu, Mancang
A2 - Cheng, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2021
Y2 - 24 September 2021 through 26 September 2021
ER -