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

• URL: http://arxiv.org/abs/2401.11358v1
• Date: Sun, 21 Jan 2024 01:14:04 GMT
• Title: ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles
• Authors: Mahedi Kamal, Tasnim Fariha, Afrina Kabir Zinia, Md. Abu Syed, Fahim Hasan Khan, Md. Mahbubur Rahman
• Abstract summary: This study discusses a custom-built dataset that includes some unidentified vehicles in the perspective of Bangladesh. 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.
• Score: 2.932123507260722
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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