Real-Time Assessment of Cut Tobacco Alignment Orderliness Using an Improved Single-Stage Regression Model

Summary

An improved single-stage regression model is developed for real-time detection of cut tobacco alignment orderliness. The approach integrates three structural optimizations and a custom orientation prediction method to enhance detection accuracy while reducing model parameters and computational complexity, enabling efficient assessment of cut tobacco alignment in industrial production environments.

Abstract

Cut tobacco orderliness, defined as the alignment consistency of cut tobacco along the cigarette axis, is a critical factor in production quality optimization. Accurate and automated assessment of this property remains challenging due to complex strand structures and variable imaging conditions. An improved single-stage regression model is developed to enable reliable detection of cut tobacco alignment orderliness. The approach incorporates three architectural modifications: a multifrequency interactive downsampling method to preserve feature information, a triple complementary fusion strategy to enhance multiscale feature representation, and a dynamic detection head to improve localization across different scales. In addition, a custom orientation prediction method is implemented to quantify alignment orderliness. The model is evaluated on an industrial image dataset, achieving a mean average precision of 89.9%, a precision of 90.6%, and a recall of 88.7%. Compared with the baseline model, the proposed approach reduces model parameters by 30.8% (from 2.6M to 1.8M) and computational complexity by 21.5% (from 6.5G to 5.1G), while improving performance. Overall, the method enables efficient and accurate assessment of cut tobacco alignment orderliness and demonstrates suitability for real-time industrial applications.

Introduction

Cut tobacco orderliness refers to the degree of axial alignment of cut tobacco within a cigarette. Understanding the factors that influence this property, such as tobacco morphology and pneumatic conveying parameters, is essential for optimizing the cutting and assembly processes. Consequently, investigating these influencing factors has become a key strategy for tobacco enterprises aiming to reduce costs and improve production efficiency. Traditional assessment of cut tobacco orderliness relies on manual methods, which are inefficient and often lack consistency and accuracy. Therefore, there is an urgent need in the industry for an automated, precise, and real-time d....

Protocol

This study did not involve human subjects, vertebrate animals, or regulated tissues; therefore, no ethical approval or institutional review board approval was required. The dataset used in this study was obtained from Longyan Tobacco Industry Co., Ltd. Images were collected from five independent production batches over a three-month period. Sampling was performed randomly at different times of day (morning, afternoon, and night) to capture natural variations in production conditions. The dataset includes samples covering a tobacco moisture range of 12%–18%, cutting speeds of 1.5, 2.0, and 2.5 m/s, and pneumatic conveying pressures of 0.4, 0.5, and 0.6 MPa, thereby ens....

Results

Experimental Environment
All experiments were conducted using the PyTorch deep learning framework, with detailed hardware and software configurations summarized in Table 1. A total of 634 images were randomly divided into training, validation, and test sets at a ratio of 7:2:1. During training, input images were uniformly resized to 896 × 1600 pixels, and the initial learning rate was set to 0.01. The stochastic gradient descent optimizer, with a momentum coefficient of 0.912, was ad.......

Discussion

A key advantage of the proposed model in cut tobacco orderliness detection is its balance between detection accuracy and computational efficiency. This task requires real-time processing of numerous fine-grained cut tobacco in high-resolution images, imposing stringent demands on model precision and speed. As shown in Table 2, the proposed model achieves the highest mAP@0.5 (89.9%) for detecting cut tobacco and steel supports among the compared single-stage detectors, while also having the fewest paramet.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Code reader	Hikvision	ID5050	Used to read barcodes on pallets; requires 24 V power supply
Dataset (cut tobacco images)	Longyan Tobacco Industrial Co., Ltd.	N/A	Custom image dataset of cut tobacco used for model training, validation, and testing
Deep learning framework (PyTorch 2.1.0)	PyTorch Foundation	Open-source software	Used for deep learning model development and training
Industrial camera	Hikvision	MV-CH120-10GC	Used for image acquisition; requires 24 V power supply
Industrial computer	Yanzhi Technology	PC1010-AX360	Used for image processing, model inference, and data storage
LED light source	Hikrobot	MV-LRDS-H-95-30-W	Strip light source providing stable illumination for imaging
Lens	Hikvision	MVL-KF0818M-12MP	Focal length: 8 mm; aperture range: F1.8–F16; 12 MP resolution
Metal brackets	Longyan Tobacco Industrial Co., Ltd.	7075-T6 Aluminum Alloy	Used for mounting and stabilizing hardware components
Network cables	Longyan Tobacco Industrial Co., Ltd.	RJ45 Crystal Head	Used to connect industrial computer, camera, and network devices
Python (version 3.9)	Python Software Foundation	Open-source software	Programming environment for implementation
Switch	TP-Link	TL-SH1005	8 Ethernet ports; requires 220 V power supply
Wireless access point (industrial communication module)	Shandong Huachuang Xunlian	BH-ANT5158S-14HV; BH-MS-AC1600HWH	Enables wireless communication between camera and industrial computer
Camera control software (MVS)	Hikvision	V4.5.1	Used for camera debugging, parameter configuration, and data acquisition
Vision processing software (Vision Master)	Hikvision	V4.3.0	Used for template matching and image result detection
Integrated development environment (PyCharm)	JetBrains	2020.1.3 x64	Used for model development and system implementation
CUDA Toolkit (version 12.1)	NVIDIA	Software toolkit (no catalog number)	Enables GPU acceleration for training and inference