Open Access

ARTICLE

Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

by Asifa Mehmood Qureshi¹, Abdul Haleem Butt¹, Abdulwahab Alazeb², Naif Al Mudawi², Mohammad Alonazi³, Nouf Abdullah Almujally⁴, Ahmad Jalal¹, Hui Liu^5,*

1 Faculty of Computing and AI, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia
3 Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
4 Department of Information System, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Cognitive Systems Lab, University of Bremen, Bremen, 28359, Germany

* Corresponding Author: Hui Liu. Email:

(This article belongs to the Special Issue: Multimodal Learning in Image Processing)

Computers, Materials & Continua 2024, 80(2), 3315-3332. https://doi.org/10.32604/cmc.2024.052582

Received 07 April 2024; Accepted 18 June 2024; Issue published 15 August 2024

Abstract

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management. However, vehicles come in a range of sizes, which is challenging to detect, affecting the traffic monitoring system’s overall accuracy. Deep learning is considered to be an efficient method for object detection in vision-based systems. In this paper, we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5 (YOLOv5) detector combined with a segmentation technique. The model consists of six steps. In the first step, all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the images. These pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase. A single-stage detector YOLOv5 is used to detect and locate vehicles in images. Each detection was exposed to Speeded Up Robust Feature (SURF) feature extraction to track multiple vehicles. Based on this, a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching technique. Further, we implemented a Kalman filter to track multiple vehicles. In the end, the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking algorithm. The experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art systems. The proposed implemented system provided 94.1% detection precision for Roundabout and 96.1% detection precision for Vehicle Aerial Imaging from Drone (VAID) datasets, respectively.

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Keywords

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