@article{ref1,
title="Object detection by attention-guided feature fusion network",
journal="Symmetry (Basel)",
year="2022",
author="Shi, Yuxuan and Fan, Yue and Xu, Siqi and Gao, Yue and Gao, Ran",
volume="14",
number="5",
pages="e887-e887",
abstract="One of the most noticeable characteristics of security issues is the prevalence of &quot;Security Asymmetry&quot;. The safety of production and even the lives of workers can be jeopardized if risk factors aren't detected in time. Today, object detection technology plays a vital role in actual operating conditions. For the sake of warning danger and ensuring the work security, we propose the Attention-guided Feature Fusion Network method and apply it to the Helmet Detection in this paper. AFFN method, which is capable of reliably detecting objects of a wider range of sizes, outperforms previous methods with an mAP value of 85.3% and achieves an excellent result in helmet detection with an mAP value of 62.4%. From objects of finite sizes to a wider range of sizes, the proposed method achieves &quot;symmetry&quot; in the sense of detection.<p /> <p>Language: en</p>",
language="en",
issn="2073-8994",
doi="10.3390/sym14050887",
url="http://dx.doi.org/10.3390/sym14050887"
}