Working Paper Article Version 1 This version is not peer-reviewed

Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3

Version 1 : Received: 11 March 2020 / Approved: 12 March 2020 / Online: 12 March 2020 (08:57:09 CET)

How to cite: Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A. Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. Preprints 2020, 2020030202 Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A. Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. Preprints 2020, 2020030202

Abstract

In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV's altitude, camera resolution, and object size. The objective of this work is to conduct a robust comparison between these two cutting-edge algorithms. By using a variety of metrics, we show that YOLOv3 yields better performance in most configurations, except that it exhibits a lower recall and less confident detections when object sizes and scales in the testing dataset differ largely from those in the training dataset.

Subject Areas

Car Detection; Convolutional Neural Networks; Deep Learning; Faster R-CNN; Unmanned Aerial Vehicles; You Only Look Once (Yolo).

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