Deep Ensembles for Semantic Segmentation on Road Detection

Deniz Uzun, Dewei Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semantic segmentation is a significant technique that can provide valuable insights into the context of driving scenes. This work discusses several mechanisms: data augmentation, transfer learning, transposed convolutions and
focal loss function for improving the performance of neural networks for image segmentation. Experiments on two traditional model architectures – U-net and MobileUNetV2 – are conducted and the results are evaluated in terms of –
Intersection-over-Union (IoU) and F-score. The KITTI Road dataset is utilised for training and testing the algorithms on road segmentation. More specifically, data augmentation and the task-specific focal loss provide the highest improvement
of 6.68% and 5.23%, respectively. To further enhance segmentation performance, an ensemble scheme is adopted where several models are executed simultaneously and their outputs are fused together to derive the final prediction. Such a design can reduce incorrect predictions of individual models and produce more precise segmentation masks
Original languageEnglish
Title of host publicationProceedings of the 26 th International Conference on Automation & Computing
PublisherIEEE Explore
Publication statusAccepted/In press - 15 Jun 2021
Event26th IEEE International Conference on Automation and Computing - University of Portsmouth, Portsmouth, United Kingdom
Duration: 2 Sep 20214 Sep 2021
Conference number: 26th
http://www.cacsuk.co.uk/index.php/icac2021

Conference

Conference26th IEEE International Conference on Automation and Computing
Abbreviated titleICAC'21
CountryUnited Kingdom
CityPortsmouth
Period2/09/214/09/21
Internet address

Keywords

  • segmentation
  • focal loss
  • transfer learning
  • ensmeble scheme
  • data augmentation

Fingerprint

Dive into the research topics of 'Deep Ensembles for Semantic Segmentation on Road Detection'. Together they form a unique fingerprint.

Cite this