Lightweight Coal Mine Safety Helmet Detection Method Based on Attention and Reconfiguration Feature Fusion

被引：0

作者：

Dong, Yanqiang ^{[1
]}

Cheng, Deqiang ^{[2
]}

Zhang, Yunhe ^{[2
]}

Kou, Qiqi ^{[3
]}

Zhang, Haoxiang ^{[2
]}

机构：

[1] Open-Pit Mine, Inner Mongolia Baiyinhua Coal Power Co., Ltd., State Power Investment Group, Xilingol League, Inner Mongolia, 026200, China

[2] School of Information and Control Engineering, China University of Mining and Technology, Jiangsu, Xuzhou,221116, China

[3] School of Computer Science and Technology, China University of Mining and Technology, Jiangsu, Xuzhou,221116, China

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 15期

关键词：

Object detection - Safety devices - Statistical tests;

D O I：

10.3778/j.issn.1002-8331.2304-0421

中图分类号：

学科分类号：

摘要：

Aiming at the problems of large model parameters, long inference interaction time, high error detection and missing rate, a lightweight real-time detection method MS-YOLO for coal mine safety helmet based on attention and reconfiguration features fusion is proposed. Firstly, to compress the size of the model and improve the detection speed without affecting the detection accuracy, a lightweight network MobileNeXt is adopted as the backbone network of the MS-YOLO algorithm. Then, the feature path fusion network PANet is reconstructed, and a new scale input, ULSAM-4 attention module and depth-separable convolution are further added to the network. Moreover, to accelerate the convergence rate of the model and improve the regression accuracy of the prediction frame, a new loss function CLIoU loss is also proposed. Furthermore, a helmet detection dataset for mine scene is established to be suitable for its special working environment. Through the experimental test on the standard dataset and the self-built dataset, the results show that the proposed MS-YOLO not only maintains high detection accuracy, but also has the advantages of good real-time performance and lightweight model. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：297 / 306