Work

IndiVNet A region adaptive semantic image segmentation for autonomous driving in unstructured environments

Year: 2025

Type: article

Abstract: Autonomous navigation in developing regions is challenged by heterogeneous traffic, dynamic occlusions, and weak road structure. Existing segmentation models, largely trained on structured Western datasets, struggle to generalize under these conditions. To address this gap, we propose IndiVNet, a semantic segmentation architecture tailored for unstructured Indian driving environments. IndiVNet introduces a progressive dilation encoder (6→16) that captures fine-grained details and broad contextual cues without inducing oversparsity. Evaluated on the India Driving Dataset (IDD), it achieves 69.98% mIoU, outperforming CNN and Transformer baselines, and reaches 73.2% mIoU on CAMVID, demonstrating strong cross-domain generalization. By combining contextual adaptability with real-time efficiency, IndiVNet offers a scalable, region-aware solution for robust autonomous navigation in complex environments.

Source: Scientific Reports

DOI: 10.1038/s41598-025-32305-2

Authors: Pritam Chakraborty, Anjan Bandyopadhyay, Siddhartha Bhattacharyya, Jan Platos

Institutions: KIIT University, VSB - Technical University of Ostrava

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,690