MassMIND: Massachusetts Maritime INfrared Dataset
Shailesh Nirgudkar, Michael DeFilippo, Michael Sacarny, Michael, Benjamin, Paul Robinette

TL;DR
MassMIND is a new labeled dataset of over 2,900 Long Wave Infrared images capturing diverse coastal maritime environments, designed to enhance deep learning-based scene understanding for autonomous marine vehicles.
Contribution
This paper introduces the first publicly available LWIR maritime dataset with instance segmentation labels, filling a critical gap in maritime autonomous vehicle research.
Findings
Dataset covers diverse environmental conditions.
Evaluation of three deep learning models demonstrates its utility.
Potential to improve maritime scene understanding in autonomous systems.
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…
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Taxonomy
TopicsMaritime Navigation and Safety · Oil Spill Detection and Mitigation · Infrared Target Detection Methodologies
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Auxiliary Classifier · Convolution · Batch Normalization · Average Pooling · Pyramid Pooling Module · Dilated Convolution · PSPNet
