Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 12, Pages 12661-12674Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3085856
Keywords
Semantics; Decoding; Training; Image segmentation; Feature extraction; Computer architecture; Estimation; Deep learning; obstacle detection; semantic segmentation; separation loss; unmanned surface vehicles (USVs)
Categories
Funding
- Slovenian Research Agency (ARRS) [P2-0214, P2-0095, J2-8175]
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The study introduces a novel deep encoder-decoder architecture, named WaSR network, specifically designed for obstacle detection in the marine environment. Through the combination of a deep encoder based on ResNet101, a decoder, and inertial information, as well as a novel loss function, the network effectively improves the segmentation accuracy of water components and achieves better segmentation results.
Obstacle detection using semantic segmentation has become an established approach in autonomous vehicles. However, existing segmentation methods, primarily developed for ground vehicles, are inadequate in an aquatic environment as they produce many false positive (FP) detections in the presence of water reflections and wakes. We propose a novel deep encoder-decoder architecture, a water segmentation and refinement (WaSR) network, specifically designed for the marine environment to address these issues. A deep encoder based on ResNet101 with atrous convolutions enables the extraction of rich visual features, while a novel decoder gradually fuses them with inertial information from the inertial measurement unit (IMU). The inertial information greatly improves the segmentation accuracy of the water component in the presence of visual ambiguities, such as fog on the horizon. Furthermore, a novel loss function for semantic separation is proposed to enforce the separation of different semantic components to increase the robustness of the segmentation. We investigate different loss variants and observe a significant reduction in FPs and an increase in true positives (TPs). Experimental results show that WaSR outperforms the current state of the art by approximately 4% in F1 score on a challenging unmanned surface vehicle dataset. WaSR shows remarkable generalization capabilities and outperforms the state of the art by over 24% in F1 score on a strict domain generalization experiment.
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