Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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 bean 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.