Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Naif Al Mudawi, Asifa Mehmood Qureshi, Maha Abdelhaq (), Abdullah Alshahrani, Abdulwahab Alazeb, Mohammed Alonazi () and Asaad Algarni
Additional contact information
Naif Al Mudawi: Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia
Asifa Mehmood Qureshi: Department of Creative Technologies, Air University, E-9, Islamabad 44000, Pakistan
Maha Abdelhaq: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdullah Alshahrani: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia
Abdulwahab Alazeb: Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia
Mohammed Alonazi: Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
Asaad Algarni: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia

Sustainability, 2023, vol. 15, issue 19, 1-19

Abstract: Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research.

Keywords: YOLOv5; vehicle detection; classification; segmentation; DBN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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