ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles

TL;DR

This paper introduces ANNA, a new deep learning dataset tailored for heterogeneous traffic in Bangladesh, demonstrating improved model accuracy over existing datasets for autonomous vehicle applications.

Contribution

The creation of ANNA dataset, including unidentified vehicles specific to Bangladesh, and validation showing its superiority over KITTI and COCO datasets for local traffic conditions.

Findings

Model trained on ANNA outperforms others in accuracy.

ANNA dataset captures unique local traffic scenarios.

Enhanced object detection for autonomous vehicles in Bangladesh.

Abstract

Recent breakthroughs in artificial intelligence offer tremendous promise for the development of self-driving applications. Deep Neural Networks, in particular, are being utilized to support the operation of semi-autonomous cars through object identification and semantic segmentation. To assess the inadequacy of the current dataset in the context of autonomous and semi-autonomous cars, we created a new dataset named ANNA. This study discusses a custom-built dataset that includes some unidentified vehicles in the perspective of Bangladesh, which are not included in the existing dataset. A dataset validity check was performed by evaluating models using the Intersection Over Union (IOU) metric. The results demonstrated that the model trained on our custom dataset was more precise and efficient than the models trained on the KITTI or COCO dataset concerning Bangladeshi traffic. The research presented in this paper also emphasizes the importance of developing accurate and efficient object detection algorithms for the advancement of autonomous vehicles.