Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

doi:10.3389/fnins.2023.1243847

LZUFE-KMS

	Truck model recognition for an automatic overload detection system based on the improved MMAL-Net
	Sun, Jiachen 1; Su, Jin 2; Yan, Zhenhao 1; Gao, Zenggui 1; Sun, Yanning 1; Liu, Lilan 1
	2023-08-10
发表期刊	FRONTIERS IN NEUROSCIENCE
卷号	17
摘要	Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety.
关键词	overload detection truck model recognition automatic weighing station fine-grained visual categorization MMAL-Net
DOI	10.3389/fnins.2023.1243847
收录类别	SCIE
语种	英语
WOS研究方向	Neurosciences & Neurology
WOS类目	Neurosciences
WOS记录号	WOS:001053620500001
出版者	FRONTIERS MEDIA SA
原始文献类型	Article
EISSN	1662-453X
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/35168
专题	兰州财经大学
通讯作者	Liu, Lilan
作者单位	1.Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Intelligent Mfg & Robot, Shanghai, Peoples R China; 2.LanZhou Univ Finance & Econ, Coll Informat Engn, Lanzhou, Peoples R China
推荐引用方式 GB/T 7714	Sun, Jiachen,Su, Jin,Yan, Zhenhao,et al. Truck model recognition for an automatic overload detection system based on the improved MMAL-Net[J]. FRONTIERS IN NEUROSCIENCE,2023,17.
APA	Sun, Jiachen,Su, Jin,Yan, Zhenhao,Gao, Zenggui,Sun, Yanning,&Liu, Lilan.(2023).Truck model recognition for an automatic overload detection system based on the improved MMAL-Net.FRONTIERS IN NEUROSCIENCE,17.
MLA	Sun, Jiachen,et al."Truck model recognition for an automatic overload detection system based on the improved MMAL-Net".FRONTIERS IN NEUROSCIENCE 17(2023).