Real-time Object Detection Model for Cigarette Brand Identification Based on an Improved Single-stage Regression Architecture

Summary

To address challenges like occlusion and lighting changes in automated warehouses, this paper introduces an improved Single-stage Regression Architecture for cigarette box brand detection. By integrating adaptive downsampling, an inverted efficient multi-scale attention mechanism, and a dynamic detection head, the proposed model achieves accurate, real-time Intelligent stocktaking.

Abstract

Visual recognition for automated cigarette inventory faces significant hurdles, including illumination changes, diverse box dimensions, and partial feature occlusion, which complicate brand verification and misplaced box detection. This article proposes an improved real-time detection model to deal with image recognition problems and improve accuracy. Firstly, an adaptive downsampling module is deployed to replace the downsampling convolution module in both the backbone and neck networks of YOLO series as the baseline or original detector, which effectively retains more feature details and realizes the lightweight of the model. Secondly, an inverted efficient multi-scale attention module is introduced to capture the spatial context information of different scales and generate a more accurate spatial attention map, which improves the prediction accuracy of the baseline model for complex features and occlusion targets. Finally, a dynamic detection head module replaces theoriginal detection head of the baseline model and performs multi-scale object detection on the feature map extracted from the backbone and neck networks to achieve accurate positioning and category division of the predicted target. To evaluate the performance of the improved model in the field, we constructed a visual dataset of the cigarette box brand. The dataset was augmented using region-specific copy-paste and traditional augmentation techniques, and the obtained dataset includes complex background, occlusion, and overlap, small target, and other factors. The experiment demonstrates that the improved model presented in this article effectively meets the requirements for real-time detection in the field. The proposed model achieves a mAP of 97.9%, with parameters and FLOPs of 1,849,679 and 5.1 G, respectively. Compared with the baseline model, the proposed model improves mAP by 0.9% while reducing parameters by 28.78% and floating-point operations by 1.4 G. Additionally, the model reaches an inference speed of 38.5 FPS, satisfying the requirements for real-time industrial detection.

Introduction

Modern manufacturing and logistics heavily rely on Automated Storage and Retrieval Systems (AS/RS)¹ to achieve high-density storage, efficient material handling, and accurate inventory management. AS/RS has become a crucial means that can help enterprises improve warehouse efficiency and reduce costs, owing to its high efficiency and intelligent characteristics. With a foundation of multi-layer shelves, various loading and unloading equipment, the AS/RS is a computer-controlled mechatronics system, which integrates multiple technologies including mechanics, electronics, computer science, communications, networking, sensors, and automatic contro....

Protocol

The dataset used in this article was acquired from the AS/RS field of the Logistics Department of Longyan Tobacco Industry Co., Ltd. This study did not involve human participants or animals. Ethical approval was not required.

Dataset acquisition and setup
Hardware configuration: Mount an industrial camera (e.g., MV-CA013-A0GM) equipped with an 8 mm focal length lens (e.g., MVL-HF0828M-6MPE) onto the cargo platform of the stacker crane. Connect the camera to a control PC via a GigE cable and a wireless AP device (e.g., BH-ANT5158S-14HV, BH-MS-AC1600HWH). Position a strip LED light (e.g., MV-LLDS-1002-38-W) around the cam....

Results

Experimental environment and parameter setting
Experiments to verify the performance of the proposed model were conducted on the deep learning framework PyTorch, and details of the experimental environment are listed in Table 1. Here, we used a special dataset containing 6,602 images that was randomly split into training, validation, and test sets at a ratio of 7:2:1. All experiments used input images with a resolution of 640 x 640 with an initial learning rate of 0.01, which was opt.......

Discussion

Accuracy-efficiency trade-off
A central achievement of this model is its superior balance between accuracy and computational efficiency. As evidenced in Table 2, the presented model achieves the highest mAP while simultaneously having the lowest number of parameters and the second-lowest FLOPs among the single-stage detector models compared. This translates directly to practical benefits: a smaller model is faster to deploy, requires less memory, and has lower inference latency and.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
code reader	Hikvision	ID5050	Hikvision equipment: Used to read barcodes on pallets, Need to connect 24V power supply
industrial camera	Hikvision	MV-CA013-A0GM, MV-CS016-10GM	Need to connect 24V power supply
LED light	HIKROBOT	MV-LLDS-1002-38-W	A one meter strip light source provides sufficient lighting conditions for the camera
lens	Hikvision	MVL-HF0828M-6MPE	focal length:8mm, Aperture range:F2.8~F16, pixel:6 million
MVS	Hikvision	V4.5.1	Hikvision software: Used for debugging cameras, setting camera parameters, and data collection
Pycharm	JetBrains	2020.1.3 x64	Used for training deep learning models and developing detection systems
Several metal brackets	Longyan Tobacco Industrial Co., Ltd.	7075-T6 Aluminum Alloy	Used for fixing cameras and code readers
Several network cables	Longyan Tobacco Industrial Co., Ltd.	RJ45 Crystal Head	Connect the base station between the industrial computer and wireless AP, connect the camera and switch, etc
Swicth	TP-Link	TL-SH1005	There are 8 Ethernet cable interfaces that require 220V power supply
Vision Master	Hikvision	V4.3.0	Hikvision software: Used for template matching and detecting image results
wireless AP device	Shandong Huachuangxunlian	BH-ANT5158S-14HV, BH-MS-AC1600HWH	Due to the large-scale movement required by the stacker crane, the network cable cannot connect the camera to the industrial computer, and a wireless AP device is needed to ensure the smooth operation of the camera network