Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery

• URL: http://arxiv.org/abs/2212.13170v1
• Date: Mon, 26 Dec 2022 14:20:32 GMT
• Title: Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery
• Abstract summary: Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras and deep-learning based algorithms represent an efficient alternative for identifying and segmenting objects of interest. Standard approaches to training these algorithms require large-scale datasets of densely labeled infrared maritime images. In this work we demonstrate that, in the context of segmenting ships in infrared imagery, weakly-supervising an algorithm with sparsely labeled data can drastically reduce data labeling costs.
• Score: 9.01037793978146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The United States coastline spans 95,471 miles; a distance that cannot be effectively patrolled or secured by manual human effort alone. Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras and deep-learning based algorithms represent a more efficient alternative for identifying and segmenting objects of interest - namely, ships. However, standard approaches to training these algorithms require large-scale datasets of densely labeled infrared maritime images. Such datasets are not publicly available and manually annotating every pixel in a large-scale dataset would have an extreme labor cost. In this work we demonstrate that, in the context of segmenting ships in infrared imagery, weakly-supervising an algorithm with sparsely labeled data can drastically reduce data labeling costs with minimal impact on system performance. We apply weakly-supervised learning to an unlabeled dataset of 7055 infrared images sourced from the Naval Air Warfare Center Aircraft Division (NAWCAD). We find that by sparsely labeling only 32 points per image, weakly-supervised segmentation models can still effectively detect and segment ships, with a Jaccard score of up to 0.756.

Related papers

• LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection [53.811713750107934]
  We propose LCMamNet, a lightweight cross-scale Mamba network for infrared small target detection.<n>A compact hierarchical encoder with cross-shaped directional bottleneck residual blocks strengthens direction-sensitive target structures.<n>Experiments on IRSTD-1k, NUAA-SIRST, and NUDT-SIRST show that the proposed network achieves mIoU scores of 71.25%, 79.60%, and 95.58%, respectively.
  arXiv  Detail & Related papers  (2026-07-27T09:06:14Z)
• Real-Time Wildfire Localization on the NASA Autonomous Modular Sensor using Deep Learning [4.4145031895964415]
  We introduce a human-annotated dataset from the NASA Autonomous Modular Sensor (AMS)<n>Our dataset combines spectral data from 12 different channels, including infrared (IR), short-wave IR (SWIR), and thermal.<n>We demonstrate results from a deep-learning model to automate the human-intensive process of fire perimeter determination.
  arXiv  Detail & Related papers  (2026-01-20T20:56:34Z)
• NASTaR: NovaSAR Automated Ship Target Recognition Dataset [1.8957631297817699]
  This dataset comprises of 3415 ship patches extracted from NovaSAR S-band imagery.<n>It includes distinctive features such as 23 unique classes, inshore/offshore separation, and an auxiliary wake dataset for patches where ship wakes are visible.<n>We validated the dataset applicability across prominent ship-type classification scenarios using benchmark deep learning models.
  arXiv  Detail & Related papers  (2025-12-20T20:42:30Z)
• Towards Foundational Models for Single-Chip Radar [49.896124982717716]
  mmWave radars are compact, inexpensive, and durable sensors that work regardless of environmental conditions, such as weather and darkness.<n>This comes at the cost of poor angular resolution, especially for inexpensive single-chip radars, which are typically used in automotive and indoor sensing applications.<n>We train a foundational model for 4D single-chip radar, which can predict 3D occupancy and semantic segmentation with quality that is typically only possible with much higher resolution sensors.
  arXiv  Detail & Related papers  (2025-09-15T22:06:17Z)
• Lightweight CNNs for Embedded SAR Ship Target Detection and Classification [0.0]
  On-board processing to generate higher-level products could reduce the data volume that needs to be downlinked.<n>This work proposes and evaluates neural networks designed for real-time inference on unfocused SAR data.
  arXiv  Detail & Related papers  (2025-08-14T14:55:19Z)
• IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection [55.554484379021524]
  Infrared Small Target Detection (IRSTD) task falls short in achieving satisfying performance due to a notable domain gap between natural and infrared images. We propose the IRSAM model for IRSTD, which improves SAM's encoder-decoder architecture to learn better feature representation of infrared small objects.
  arXiv  Detail & Related papers  (2024-07-10T10:17:57Z)
• Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware [1.4330085996657045]
  On-board data processing can identify ships and reduce the amount of data sent to the ground. Most images captured on board contain only bodies of water or land, with the Airbus Ship Detection dataset showing only 22.1% of images containing ships. We designed a low-power, two-stage system to optimize performance instead of relying on a single complex model.
  arXiv  Detail & Related papers  (2024-06-17T08:36:12Z)
• Image and AIS Data Fusion Technique for Maritime Computer Vision Applications [1.482087972733629]
  We develop a technique that fuses Automatic Identification System (AIS) data with vessels detected in images to create datasets. Our approach associates detected ships to their corresponding AIS messages by estimating distance and azimuth. This technique is useful for creating datasets for waterway traffic management, encounter detection, and surveillance.
  arXiv  Detail & Related papers  (2023-12-07T20:54:49Z)
• Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement [70.2429155741593]
  This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT) It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles. We propose a novel underwater image enhancement algorithm designed specifically to boost tracking quality. The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers.
  arXiv  Detail & Related papers  (2023-08-30T07:41:26Z)
• MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection [42.92798053154314]
  We develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and 17598 pixel-level tiny ship annotations. Considering the extreme characteristics of those tiny ships in such challenging scenes, we propose a multi-level TransUNet (MTU-Net) in this paper. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning based SIRST methods in terms of probability of detection, false alarm rate and intersection over union.
  arXiv  Detail & Related papers  (2022-09-28T00:48:14Z)
• xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery [52.67592123500567]
  Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. It is now possible to automate detection of dark vessels day or night, under all-weather conditions. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission.
  arXiv  Detail & Related papers  (2022-06-02T06:53:45Z)
• LLVIP: A Visible-infrared Paired Dataset for Low-light Vision [4.453060631960743]
  We present LLVIP, a visible-infrared paired dataset for low-light vision. This dataset contains 30976 images, or 15488 pairs, most of which were taken at very dark scenes. We compare the dataset with other visible-infrared datasets and evaluate the performance of some popular visual algorithms.
  arXiv  Detail & Related papers  (2021-08-24T16:29:17Z)
• Multi-View Radar Semantic Segmentation [3.2093811507874768]
  Automotive radars are low-cost active sensors that measure properties of surrounding objects. They are seldom used for scene understanding due to the size and complexity of radar raw data. We propose several novel architectures, and their associated losses, which analyse multiple "views" of the range-angle-Doppler radar tensor to segment it semantically.
  arXiv  Detail & Related papers  (2021-03-30T09:56:41Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning [59.19469551774703]
  Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. We construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439 RGB-Infrared image pairs, covering urban roads, residential areas, parking lots, and other scenarios from day to night.
  arXiv  Detail & Related papers  (2020-03-05T05:29:44Z)
• RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training [77.62171090230986]
  We propose an easily scalable and self-supervised technique that can be used to pre-train any semantic RGB segmentation method. In particular, our pre-training approach makes use of automatically generated labels that can be obtained using depth sensors. We show how our proposed self-supervised pre-training with HN-labels can be used to replace ImageNet pre-training.
  arXiv  Detail & Related papers  (2020-02-06T11:16:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.