Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 11; p. 2577
• Main Authors: Sharma, Suvash, Ball, John E, Tang, Bo, Carruth, Daniel W, Doude, Matthew, Islam, Muhammad Aminul
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06-06-2019; MDPI
• Subjects: Algorithms; Architectural engineering; autonomous; Autonomous vehicles; Classification; Computer engineering; Computer simulation; Computer vision; Datasets; Deep learning; Domains; International conferences; Knowledge management; Neural networks; off-road driving; Remote sensing; Roads & highways; Robotics; Semantic segmentation; Semantics; transfer learning; Weight reduction; United States > US; Mississippi; transfer learning; autonomous; semantic segmentation; off-road driving
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19112577

Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However, this method may not be a straight-forward task considering several issues such as original network size or large differences between the source and target domain. In this paper, we perform transfer learning for semantic segmentation of off-road driving environments using a pre-trained segmentation network called DeconvNet. We explore and verify two important aspects regarding transfer learning. First, since the original network size was very large and did not perform well for our application, we proposed a smaller network, which we call the light-weight network. This light-weight network is half the size to the original DeconvNet architecture. We transferred the knowledge from the pre-trained DeconvNet to our light-weight network and fine-tuned it. Second, we used synthetic datasets as the intermediate domain before training with the real-world off-road driving data. Fine-tuning the model trained with the synthetic dataset that simulates the off-road driving environment provides more accurate results for the segmentation of real-world off-road driving environments than transfer learning without using a synthetic dataset does, as long as the synthetic dataset is generated considering real-world variations. We also explore the issue whereby the use of a too simple and/or too random synthetic dataset results in negative transfer. We consider the Freiburg Forest dataset as a real-world off-road driving dataset.