报告详情
YOLO-AGU: A Lightweight Target Detector for Critical Components in Artillery Autoloading Systems
编号：38
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更新：2024-10-23 10:50:43
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口头报告
报告开始：2024年11月01日 17:00（Asia/Shanghai）
报告时间：20min
所在会场：[P4] Parallel Session 4 [P4-1] Parallel Session 4（November 1 PM）
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摘要
Abstract—Artillery automatic loading system is an important part of the artillery and an indispensable part of the artillery intelligence process. The complex combat environment of the artillery autoloading chamber environment and the high impact of the combat mission of each component are prone to various failures, so it is necessary to use the visual system to assist in fault diagnosis. Problems such as occlusion and illumination changes make it challenging to monitor the health status of each component of automatic loading in a complex environment. To address these issues, this paper proposes a vision inspection method suitable for harsh imaging environments that can accurately identify critical components within an autoloading chamber in real time. Based on YOLOv8, this paper introduces a lightweight image preprocessing module, AOD-Net, to perform preliminary enhancement processing on the input image before feature extraction to improve the target detection performance of YOLOv8 under harsh imaging environment. Meanwhile, GSConv and UIB are incorporated to combine to build an efficient neck, called GU-Neck. in this architecture, GSConv combines the advantages of SC and DSC, and introduces the shuffle operation to uniformly mix the features, which enhances the feature expression ability of the model while maintaining efficient computation; the UIB module can further reduce the computational cost and the redundant of gradient information transfer and improve the computational efficiency and feature reuse capability. The proposed method is evaluated using a self-constructed data set of magazine structural components. The experimental results show that the YOLO-AGU model can meet the requirements for accurate real-time monitoring of structural components in harsh environments, with a 4% improvement in mAP@50-95 compared with baseline, and the model is lightweight enough to be deployed on an on-board embedded processor.
关键词
Artillery, YOLO, detection, lightweight, image preprocessing
报告人
QiuRui
Mr nanjing university of science and technology稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
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09月30日 2024
初稿截稿日期
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11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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