Slim-YOLO-PR_KD:一种高效的煤矿井下姿态变化物体检测方法

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huaxing Mu, Jueting Liu, Yanyun Guan, Wei Chen, Tingting Xu, Zehua Wang
{"title":"Slim-YOLO-PR_KD:一种高效的煤矿井下姿态变化物体检测方法","authors":"Huaxing Mu, Jueting Liu, Yanyun Guan, Wei Chen, Tingting Xu, Zehua Wang","doi":"10.1007/s11554-024-01539-0","DOIUrl":null,"url":null,"abstract":"<p>Real-time object detection in underground coal mine is a crucial task in the development of AI-assisted supervision systems. Due to the complex environment of the underground coal mine, limited computing resources, and the variability of object poses, the general object detection algorithms cannot provide good performance. Hence, an improved underground pose-varied object detection method named Slim-YOLO-PR_KD has been proposed. By designing an efficient pose-varied attention module (EPA) for the backbone network, providing a receive field block (RFB) module for the neck network, and optimizing the loss function, the underground pose-varied detection model YOLO-PR is obtained, which achieved good accuracy but reduced speed. For YOLO-PR, the study improved the original module by designing RFB_SK, a lightweight C2f_GSG module, a shared parameter detection head and selectively replaced modules to slim down the whole network, resulting in a lightweight detection model Slim-YOLO-PR. By using an attention guided knowledge distillation of underground object detection method and using YOLO-PR as the teacher model, the efficient pose-varied detection model Slim-YOLO-PR_KD for coal mine underground is proposed. The experimental results show that compared with the baseline model, the proposed Slim-YOLO-PR_KD has a faster detection speed, achieving higher detection accuracy while reducing model parameters and computational complexity by 42% and 46% respectively, making it capable of performing real-time underground detection tasks. Compared with other general detection models, Slim-YOLO-PR_KD exhibits excellent performance in real-time pose-varied object detection tasks in complex environments of underground coal mines.</p>","PeriodicalId":51224,"journal":{"name":"Journal of Real-Time Image Processing","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine\",\"authors\":\"Huaxing Mu, Jueting Liu, Yanyun Guan, Wei Chen, Tingting Xu, Zehua Wang\",\"doi\":\"10.1007/s11554-024-01539-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Real-time object detection in underground coal mine is a crucial task in the development of AI-assisted supervision systems. Due to the complex environment of the underground coal mine, limited computing resources, and the variability of object poses, the general object detection algorithms cannot provide good performance. Hence, an improved underground pose-varied object detection method named Slim-YOLO-PR_KD has been proposed. By designing an efficient pose-varied attention module (EPA) for the backbone network, providing a receive field block (RFB) module for the neck network, and optimizing the loss function, the underground pose-varied detection model YOLO-PR is obtained, which achieved good accuracy but reduced speed. For YOLO-PR, the study improved the original module by designing RFB_SK, a lightweight C2f_GSG module, a shared parameter detection head and selectively replaced modules to slim down the whole network, resulting in a lightweight detection model Slim-YOLO-PR. By using an attention guided knowledge distillation of underground object detection method and using YOLO-PR as the teacher model, the efficient pose-varied detection model Slim-YOLO-PR_KD for coal mine underground is proposed. The experimental results show that compared with the baseline model, the proposed Slim-YOLO-PR_KD has a faster detection speed, achieving higher detection accuracy while reducing model parameters and computational complexity by 42% and 46% respectively, making it capable of performing real-time underground detection tasks. Compared with other general detection models, Slim-YOLO-PR_KD exhibits excellent performance in real-time pose-varied object detection tasks in complex environments of underground coal mines.</p>\",\"PeriodicalId\":51224,\"journal\":{\"name\":\"Journal of Real-Time Image Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Real-Time Image Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11554-024-01539-0\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Real-Time Image Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11554-024-01539-0","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

煤矿井下的实时物体检测是人工智能辅助监控系统开发中的一项重要任务。由于煤矿井下环境复杂、计算资源有限、物体姿态多变,一般的物体检测算法无法提供良好的性能。因此,一种名为 Slim-YOLO-PR_KD 的改进型井下姿态多变物体检测方法被提出。通过为骨干网络设计高效的姿态变化注意模块(EPA),为颈部网络提供接收场块模块(RFB),并优化损失函数,得到了地下姿态变化检测模型 YOLO-PR,该模型精度高但速度慢。针对 YOLO-PR,研究通过设计 RFB_SK、轻量级 C2f_GSG 模块、共享参数检测头,对原有模块进行改进,并有选择地替换模块,使整个网络瘦身,从而得到轻量级检测模型 Slim-YOLO-PR。利用注意力引导下的井下物体检测知识提炼方法,以 YOLO-PR 为教师模型,提出了适用于煤矿井下的高效姿态变化检测模型 Slim-YOLO-PR_KD。实验结果表明,与基线模型相比,所提出的 Slim-YOLO-PR_KD 检测速度更快,检测精度更高,同时模型参数和计算复杂度分别降低了 42% 和 46% ,能够胜任井下实时检测任务。与其他一般检测模型相比,Slim-YOLO-PR_KD 在煤矿井下复杂环境下的实时姿态变化物体检测任务中表现出优异的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine

Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine

Real-time object detection in underground coal mine is a crucial task in the development of AI-assisted supervision systems. Due to the complex environment of the underground coal mine, limited computing resources, and the variability of object poses, the general object detection algorithms cannot provide good performance. Hence, an improved underground pose-varied object detection method named Slim-YOLO-PR_KD has been proposed. By designing an efficient pose-varied attention module (EPA) for the backbone network, providing a receive field block (RFB) module for the neck network, and optimizing the loss function, the underground pose-varied detection model YOLO-PR is obtained, which achieved good accuracy but reduced speed. For YOLO-PR, the study improved the original module by designing RFB_SK, a lightweight C2f_GSG module, a shared parameter detection head and selectively replaced modules to slim down the whole network, resulting in a lightweight detection model Slim-YOLO-PR. By using an attention guided knowledge distillation of underground object detection method and using YOLO-PR as the teacher model, the efficient pose-varied detection model Slim-YOLO-PR_KD for coal mine underground is proposed. The experimental results show that compared with the baseline model, the proposed Slim-YOLO-PR_KD has a faster detection speed, achieving higher detection accuracy while reducing model parameters and computational complexity by 42% and 46% respectively, making it capable of performing real-time underground detection tasks. Compared with other general detection models, Slim-YOLO-PR_KD exhibits excellent performance in real-time pose-varied object detection tasks in complex environments of underground coal mines.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
审稿时长
6 months
期刊介绍: Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信