Sim2Air - Synthetic aerial dataset for UAV monitoring

TL;DR

This paper introduces Sim2Air, a synthetic aerial dataset generated with shape-focused object representation and texture randomization, improving UAV monitoring detection performance under challenging conditions.

Contribution

It presents a novel synthetic dataset creation method emphasizing shape over texture, enhancing UAV detection models' robustness in difficult lighting and small object scenarios.

Findings

Increased mAP for YOLO and Faster R-CNN on real UAV datasets.

Texture randomization improves detection under challenging illumination.

Shape-based representation enhances generalization across domains.

Abstract

In this paper we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photorealism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely challenging illumination and objects occupying only a small portion of the image. Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic data with randomized textures confirmed our approach by increasing the mAP value (17 and 3.7 percentage points for YOLO; 20 and 1.1 percentage points for Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV images with motion blur. Testing on different domains, we conclude that the more the generalisation ability is put to the test, the more apparent are the advantages of the shape-based representation.