@inproceedings{ea87914dfcd84ec69a4904f222760e05,
title = "Research on Infrared Small Target Detection Algorithm and Model Lightweight",
abstract = "In order to improve the detection accuracy of the existing algorithm YOLOv5s for small infrared targets and lightweight its model, YOLOv5s-SIT algorithm is designed to optimize the network structure, including ECBAM attention module that enhances important features and suppresses non-important features, and spp\_x fusion module to enrich the expression ability of feature maps etc. The lightweight model first performs sparse training on the BN layer, and then prunes and compresses the feature extraction backbone network of the algorithm without changing the integrity of the model. Experimental results show that the algorithm can achieve higher detection accuracy, faster detection speed, and lightweight model volume.",
keywords = "Infrared small target, Model lightweight, Sparse, Target detection",
author = "Shuo Han and Zhiqiang Guo and Bo Mo and Jie Zhao",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023 ; Conference date: 27-08-2023 Through 29-08-2023",
year = "2023",
doi = "10.1109/YAC59482.2023.10401765",
language = "English",
series = "Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "532--538",
booktitle = "Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023",
address = "United States",
}