Investigation of Object Detection and Identification at Different Lighting Conditions for Autonomous Vehicle Application

IF 1 Q4 ENGINEERING, MECHANICAL
Noorfadzli Abdul Razak, Nur Alya Aqilah Sabri, Juliana Johari, Fazlina Ahmat Ruslan, Mahanijah Md. Kamal, Mohd Azri Abdul Aziz
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引用次数: 0

Abstract

Ensuring the safety of autonomous vehicles requires effective detection and tracking of surrounding objects. This paper proposes the design and development of a driverless transportation system module focused on identifying obstacles around vehicles. By integrating computer vision with deep learning, the system presents a reliable and cost-effective solution for autonomous driving. Utilizing Raspberry Pi 4B and a USB webcam, a compact hardware setup is created for seamless implementation in autonomous vehicles. The algorithm presented in this study enables the detection, classification, and tracking of both moving and stationary objects, including cars, buses, trucks, people, and motorcycles. TensorFlow Lite, a deep-learning network, is employed for efficient object detection and classification. Leveraging Python as the primary programming language, known for its high-level object-oriented features and integrated semantics, the algorithm is tailored for web and application development. Experimental results demonstrate the system’s capability to concurrently detect and identify multiple local objects with an accuracy ranging from 50% to 80% in day and night conditions. These findings underscore the potential of deep learning in advancing autonomous vehicle technology.
自动驾驶汽车不同光照条件下目标检测与识别研究
确保自动驾驶汽车的安全需要对周围物体进行有效的检测和跟踪。本文提出了一个无人驾驶交通系统模块的设计和开发,重点是识别车辆周围的障碍物。通过将计算机视觉与深度学习相结合,该系统为自动驾驶提供了可靠且具有成本效益的解决方案。利用树莓派4B和USB网络摄像头,创建了一个紧凑的硬件设置,以便在自动驾驶汽车中无缝实现。本研究中提出的算法可以检测、分类和跟踪移动和静止的物体,包括汽车、公共汽车、卡车、人和摩托车。TensorFlow Lite是一种深度学习网络,用于有效的目标检测和分类。利用Python作为主要的编程语言,以其高级面向对象的特性和集成语义而闻名,该算法是为web和应用程序开发量身定制的。实验结果表明,该系统能够在白天和夜间同时检测和识别多个局部目标,准确率在50%到80%之间。这些发现强调了深度学习在推进自动驾驶汽车技术方面的潜力。
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来源期刊
CiteScore
2.40
自引率
10.00%
发文量
43
审稿时长
20 weeks
期刊介绍: The IJAME provides the forum for high-quality research communications and addresses all aspects of original experimental information based on theory and their applications. This journal welcomes all contributions from those who wish to report on new developments in automotive and mechanical engineering fields within the following scopes. -Engine/Emission Technology Automobile Body and Safety- Vehicle Dynamics- Automotive Electronics- Alternative Energy- Energy Conversion- Fuels and Lubricants - Combustion and Reacting Flows- New and Renewable Energy Technologies- Automotive Electrical Systems- Automotive Materials- Automotive Transmission- Automotive Pollution and Control- Vehicle Maintenance- Intelligent Vehicle/Transportation Systems- Fuel Cell, Hybrid, Electrical Vehicle and Other Fields of Automotive Engineering- Engineering Management /TQM- Heat and Mass Transfer- Fluid and Thermal Engineering- CAE/FEA/CAD/CFD- Engineering Mechanics- Modeling and Simulation- Metallurgy/ Materials Engineering- Applied Mechanics- Thermodynamics- Agricultural Machinery and Equipment- Mechatronics- Automatic Control- Multidisciplinary design and optimization - Fluid Mechanics and Dynamics- Thermal-Fluids Machinery- Experimental and Computational Mechanics - Measurement and Instrumentation- HVAC- Manufacturing Systems- Materials Processing- Noise and Vibration- Composite and Polymer Materials- Biomechanical Engineering- Fatigue and Fracture Mechanics- Machine Components design- Gas Turbine- Power Plant Engineering- Artificial Intelligent/Neural Network- Robotic Systems- Solar Energy- Powder Metallurgy and Metal Ceramics- Discrete Systems- Non-linear Analysis- Structural Analysis- Tribology- Engineering Materials- Mechanical Systems and Technology- Pneumatic and Hydraulic Systems - Failure Analysis- Any other related topics.
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