FROM PIXELS TO PREDICTIONS: ROLE OF BOOSTED DEEP LEARNING-ENABLED OBJECT DETECTION FOR AUTONOMOUS VEHICLES ON LARGE SCALE CONSUMER ELECTRONICS ENVIRONMENT

Mimouna Abdullah Alkhonaini, Hanan Abdullah Mengash, Nadhem Nemri, Shouki A. Ebad, Faiz Abdullah Alotaibi, Jawhara Aljabri, Yazeed Alzahrani and Mrim M. Alnfiai
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Mimouna Abdullah Alkhonaini: Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Hanan Abdullah Mengash: ��Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Nadhem Nemri: ��Department of Information Systems, Applied College at Mahayil, King Khalid University, Saudi Arabia
Shouki A. Ebad: �Department of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia
Faiz Abdullah Alotaibi: �Department of Information Science, College of Humanities and Social Sciences, King Saud University, P. O. Box 28095, Riyadh 11437, Saudi Arabia
Jawhara Aljabri: ��Department of Computer Science, University College in Umluj, University of Tabuk, Saudi Arabia
Yazeed Alzahrani: *Department of Computer Engineering, College of Engineering in Wadi Addawasir, Prince Sattam bin Abdulaziz University, Saudi Arabia
Mrim M. Alnfiai: ��†Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-17

Abstract: Consumer electronics (CE) companies have the potential to significantly contribute to the advancement of autonomous vehicles and their accompanying technology by providing security, connectivity, and efficiency. The Consumer Autonomous Vehicles market is set for significant growth, driven by growing awareness and implementation of sustainable practices using computing technologies for traffic flow optimization in smart cities. Businesses are concentrating more on eco-friendly solutions, using AI, communication networks, and sensors for autonomous city navigation, giving safer and more efficient mobility solutions in response to growing environmental concerns. Object detection is a crucial element of autonomous vehicles and complex systems, which enables them to observe and react to their surroundings in real-time. Multiple autonomous vehicles employ deep learning (DL) for detection and deploy specific sensor arrays custom-made to their use case or environment. DL processes sensory data for autonomous vehicles, enabling data-driven decisions on environmental reactions and obstacle recognition. This paper projects a Galactical Swarm Fractals Optimizer with DL-Enabled Object Detection for Autonomous Vehicles (GSODL-OOAV) model in Smart Cities. The presented GSODL-OOAV model enables the object identification for autonomous vehicles properly. To accomplish this, the GSODL-OOAV model initially employs a RetinaNet object detector to detect the objects effectively. Besides, the long short-term memory ensemble (BLSTME) technique was exploited to allot proper classes to the detected objects. A hyperparameter tuning procedure utilizing the GSO model is employed to enhance the classification efficiency of the BLSTME approach. The experimentation validation of the GSODL-OOAV technique is verified using the BDD100K database. The comparative study of the GSODL-OOAV approach illustrated a superior accuracy outcome of 99.06% over present innovative approaches.

Keywords: Consumer Electronics; Deep Learning; Autonomous Vehicles; Object Detection; Parameter Tuning; Self-Driving; Fractal Optimization; Complex Systems; Traffic Flow; Smart Cities (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X2540047X

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