Enhancing Autonomous Vehicle Segmentation with Compact UNET Models and Temporal Input Fusion

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Abstract
This work addresses the challenge of video semantic segmentation, a critical component in applications such as autonomous driving. The primary aim was to explore the role of temporal awareness in video sequences and its impact on split computing. To achieve this, we analyzed existing deep neural networks for semantic segmentation and their computational demands and proposed a split computing architecture that leverages high-accuracy segmentation results from a remote server to enhance performance on mobile devices. To validate the proposed approach, we developed and tested four U-Net modifications on the CamVid dataset. Our results demonstrate that incorporating segmentation masks from previous frames significantly improves accuracy in split computing scenarios. In particular, masks warped using optical flow yielded the best results, increasing segmentation accuracy from 81.1% to 84.1% with minimal additional computational cost. These findings highlight the potential of time-aware split computing to enhance video semantic segmentation performance in resource-constrained IoT environments.

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