Vehicle Detection Using Different Deep Learning Algorithms from Image Sequence
Sumeyye Cepni, Muhammed Enes Atik, Zaide Duran
Abstract
Image processing has become a very popular topic in recent years with images obtained by photogrammetry, remote sensing and computer vision. Deep learning practices are progressing rapidly with this innovation. Object detection, one of the new subjects of deep learning, is applied to high resolution aerial or remote sensing images to ex.tract information from these images. Traditional convolutional neural network (CNN) methods perform estimates in two stages but remain slow in terms of speed performance. You Only Look Once (YOLO) method that is used for real-time object detection, is quickly performed using a single convolutional neural network. In this study, YOLO-v3, YOLO-v3-spp and YOLO-v3-tiny models were applied in the Google Colab environment using python programming language. The comparison of YOLO models trained on COCO data was performed on the video obtained separately from a UAV and the terrestrial camera to identify the vehicles. As a result of the study, the highest results were obtained in Yolov3-spp method with average IoU 84,88% and precision value as 72,02%.