MassMIND: Massachusetts Maritime INfrared Dataset

• URL: http://arxiv.org/abs/2209.04097v1
• Date: Fri, 9 Sep 2022 02:54:26 GMT
• Title: MassMIND: Massachusetts Maritime INfrared Dataset
• Authors: Shailesh Nirgudkar, Michael DeFilippo, Michael Sacarny, Michael Benjamin and Paul Robinette
• Abstract summary: This paper presents a labeled dataset of over 2,900 Long Wave Infrared (LWIR) segmented images captured in coastal maritime environment under diverse conditions. The images are labeled using instance segmentation and classified in seven categories -- sky, water, obstacle, living obstacle, bridge, self and background. We evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy.
• Score: 3.5751176069022983
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains -- the first being coastal waters -- with many obstacles, structures and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, Long Wave Infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2,900 LWIR segmented images captured in coastal maritime environment under diverse conditions. The images are labeled using instance segmentation and classified in seven categories -- sky, water, obstacle, living obstacle, bridge, self and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment.

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