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Research on a Lightweight Small Object Detection Method Based on Lite-RFB Modules Cover

Research on a Lightweight Small Object Detection Method Based on Lite-RFB Modules

By: Fei Wang and  Liping Lu  
Open Access
|Dec 2025

Figures & Tables

Figure 1.

SSD Network Architecture
SSD Network Architecture

Figure 2.

RFB Model Diagram
RFB Model Diagram

Figure 3.

MobileNet-SSD Model Diagram
MobileNet-SSD Model Diagram

Figure 4.

Experimental Workflow Diagram
Experimental Workflow Diagram

Figure 5.

Experimental Results Comparison
Experimental Results Comparison

Figure 6.

Test Set Training Figure
Test Set Training Figure

Experimental Environment Configuration

ParameterConfiguration
CPUIntel® Core™ i9-14900KF 3.20 GHz
GPUNVIDIA GeForce RTX 5080
Memory256GB
Graphics Memory1.82TB
System Environment64-bit operating system
Experimental PlatformPyTorch 2.1, Python 3.8, PyCharm
Acceleration EnvironmentCUDA 12.1

Object Categories in the VOC

VehiclesHouseholdAnimalsOthers
AeroplaneBottleBirdPerson
BicyleChairCat
BoatDining tableCow
CarPotted plantDog
MotorbikeSofaHorse
TrainTV/MonitorSheep
Language: English
Page range: 94 - 103
Published on: Dec 31, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Fei Wang, Liping Lu, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.