ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles
This work addresses the need for domain-specific datasets for autonomous vehicles in regions like Bangladesh, but it is incremental as it focuses on data collection rather than novel algorithms.
The authors tackled the problem of inadequate datasets for autonomous vehicles in heterogeneous traffic by creating a new dataset called ANNA, which includes unidentified vehicles from Bangladesh, and showed that models trained on it achieved higher precision and efficiency compared to KITTI or COCO datasets using the IOU metric.
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.