ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for
  Autonomous Vehicles
Mahedi Kamal Tasnim Fariha Afrina Kabir Zinia Md. Abu Syed Fahim Hasan Khan Md. Mahbubur Rahman
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.