Abstract
Object detection, semantic segmentation, and instance segmentation form the bases for many computer vision tasks in autonomous driving. The complexity of these tasks increases as we shift from object detection to instance segmentation. The state-of-the-art models are evaluated on standard datasets such as pascal-voc and ms-cococ, which do not consider the dynamics of road scenes. Driving datasets such as Cityscapes and Berkeley Deep Drive (bdd) are captured in a structured environment with better road markings and fewer variations in the appearance of objects and background. However, the same does not hold for Indian roads. The Indian Driving Dataset (idd) is captured in unstructured driving scenarios and is highly challenging for a model due to its diversity. This work presents a comprehensive evaluation of state-of-the-art models on object detection, semantic segmentation, and instance segmentation on-road scene datasets. We present our analyses and compare their quantitative and qualitative performance on structured driving datasets (Cityscapes and bdd) and the unstructured driving dataset (idd); understanding the behavior on these datasets helps in addressing various practical issues and helps in creating real-life applications.
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This work was partly funded by IHub-Data at IIIT-Hyderabad.
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Singh, D., Rahane, A., Mondal, A., Subramanian, A., Jawahar, C.V. (2022). Evaluation of Detection and Segmentation Tasks on Driving Datasets. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_44
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