Automated System for Ship Detection from Medium Resolution Satellite
Optical Imagery
- URL: http://arxiv.org/abs/2104.13923v1
- Date: Wed, 28 Apr 2021 15:06:18 GMT
- Title: Automated System for Ship Detection from Medium Resolution Satellite
Optical Imagery
- Authors: Dejan Stepec and Tomaz Martincic and Danijel Skocaj
- Abstract summary: We present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations.
This optical satellite imagery is readily available for any place on Earth and underutilized in the maritime domain, compared to existing solutions based on synthetic-aperture radar (SAR) imagery.
- Score: 3.190574537106449
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this paper, we present a ship detection pipeline for low-cost medium
resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet
Labs Dove constellations. This optical satellite imagery is readily available
for any place on Earth and underutilized in the maritime domain, compared to
existing solutions based on synthetic-aperture radar (SAR) imagery. We
developed a ship detection method based on a state-of-the-art
deep-learning-based object detection method which was developed and evaluated
on a large-scale dataset that was collected and automatically annotated with
the help of Automatic Identification System (AIS) data.
Related papers
- Object Depth and Size Estimation using Stereo-vision and Integration with SLAM [2.122581579741322]
We propose a highly accurate stereo-vision approach to complement LiDAR in autonomous robots.
The system employs advanced stereo vision-based object detection to detect both tangible and non-tangible objects.
The depth and size information is then integrated into the SLAM process to enhance the robot's navigation capabilities in complex environments.
arXiv Detail & Related papers (2024-09-11T21:12:48Z) - 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) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration [55.41644538483948]
This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles.
It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format.
The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
arXiv Detail & Related papers (2023-04-27T20:21:18Z) - Automatized marine vessel monitoring from sentinel-1 data using
convolution neural network [0.0]
We introduce wavelet transformation-based Convolution Neural Network approach to recognize objects from SAR images during the heavy naval traffic.
The information comprises Sentinel-1 SAR-C dual-polarization data acquisitions over the western coastal zones of India.
arXiv Detail & Related papers (2023-04-23T18:09:44Z) - 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) - Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge
TPU [58.720142291102135]
In this paper we propose a pose estimation software exploiting neural network architectures.
We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
arXiv Detail & Related papers (2022-04-07T08:53:18Z) - Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today.
We propose a novel deep learning algorithm to automate this aerial person detection (APD) task.
We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
arXiv Detail & Related papers (2021-11-17T21:48:31Z) - Infrared Beacons for Robust Localization [58.720142291102135]
This paper presents a localization system that uses infrared beacons and a camera equipped with an optical band-pass filter.
Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions.
arXiv Detail & Related papers (2021-04-19T14:23:20Z) - ShipSRDet: An End-to-End Remote Sensing Ship Detector Using
Super-Resolved Feature Representation [8.464914977101252]
We propose an end-to-end network named ShipSRDet to improve ship detection performance.
In our method, we not only feed the super-resolved images to the detector but also integrate the intermediate features of the SR network with those of the detection network.
arXiv Detail & Related papers (2021-03-17T14:51:45Z) - Boosting ship detection in SAR images with complementary pretraining
techniques [14.34438598597809]
We propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.
We also propose an optical-SAR matching (OSM) pretraining technique, which transfers plentiful texture features from optical images to SAR images by common representation learning.
The proposed method won the sixth place of ship detection in SAR images in 2020 Gaofen challenge.
arXiv Detail & Related papers (2021-03-15T10:03:04Z)
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.