CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask
- URL: http://arxiv.org/abs/2410.20436v1
- Date: Sun, 27 Oct 2024 13:26:44 GMT
- Title: CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask
- Authors: Yuk-Kwan Wong, Ziqiang Zheng, Mingzhe Zhang, David Suggett, Sai-Kit Yeung,
- Abstract summary: We propose CoralSCOP-LAT, an automatic and semi-automatic coral reef labeling and analysis tool.
The proposed CoralSCOP-LAT surpasses the existing tools by a large margin from analysis efficiency, accuracy, and flexibility.
Our CoralSCOP-LAT, as the first dense coral reef analysis tool in the market, facilitates repeated large-scale coral reef monitoring analysis.
- Score: 14.092526875441221
- License:
- Abstract: Images of coral reefs provide invaluable information, which is essentially critical for surveying and monitoring the coral reef ecosystems. Robust and precise identification of coral reef regions within surveying imagery is paramount for assessing coral coverage, spatial distribution, and other statistical analyses. However, existing coral reef analytical approaches mainly focus on sparse points sampled from the whole imagery, which are highly subject to the sampling density and cannot accurately express the coral ambulance. Meanwhile, the analysis is both time-consuming and labor-intensive, and it is also limited to coral biologists. In this work, we propose CoralSCOP-LAT, an automatic and semi-automatic coral reef labeling and analysis tool, specially designed to segment coral reef regions (dense pixel masks) in coral reef images, significantly promoting analysis proficiency and accuracy. CoralSCOP-LAT leverages the advanced coral reef foundation model to accurately delineate coral regions, supporting dense coral reef analysis and reducing the dependency on manual annotation. The proposed CoralSCOP-LAT surpasses the existing tools by a large margin from analysis efficiency, accuracy, and flexibility. We perform comprehensive evaluations from various perspectives and the comparison demonstrates that CoralSCOP-LAT not only accelerates the coral reef analysis but also improves accuracy in coral segmentation and analysis. Our CoralSCOP-LAT, as the first dense coral reef analysis tool in the market, facilitates repeated large-scale coral reef monitoring analysis, contributing to more informed conservation efforts and sustainable management of coral reef ecosystems. Our tool will be available at https://coralscop.hkustvgd.com/.
Related papers
- Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.
Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.
Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
arXiv Detail & Related papers (2024-10-14T17:22:55Z) - Automatic Coral Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring [0.0]
Coral reefs are vital ecosystems that are under increasing threat due to local human impacts and climate change.
In this paper, we present an automatic coral detection system utilizing the You Only Look Once deep learning model.
arXiv Detail & Related papers (2024-04-03T08:00:46Z) - Deep learning for multi-label classification of coral conditions in the
Indo-Pacific via underwater photogrammetry [24.00646413446011]
This study created a dataset representing common coral conditions and associated stressors in the Indo-Pacific.
It assessed existing classification algorithms and proposed a new multi-label method for automatically detecting coral conditions and extracting ecological information.
The proposed method accurately classified coral conditions as healthy, compromised, dead, and rubble.
arXiv Detail & Related papers (2024-03-09T14:42:16Z) - CoralVOS: Dataset and Benchmark for Coral Video Segmentation [12.434773034255455]
We propose a large-scale coral video segmentation dataset: textbfCoralVOS as demonstrated in Fig. 1.
We perform experiments on our CoralVOS dataset, including 6 recent state-of-the-art video object segmentation (VOS) algorithms.
The results show that there is still great potential for further promoting the segmentation accuracy.
arXiv Detail & Related papers (2023-10-03T10:45:37Z) - Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning [4.8902950939676675]
This paper presents a new paradigm for mapping underwater environments from ego-motion video.
We show high-precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs.
Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects.
arXiv Detail & Related papers (2023-09-22T11:35:10Z) - Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan
Klasifikasi Citra [3.254879465902239]
This study utilizes a specialized dataset consisting of 923 images collected from Flickr using the Flickr API.
The method employed in this research involves the use of machine learning models, particularly convolutional neural networks (CNN)
It was found that a from-scratch ResNet model can outperform pretrained models in terms of precision and accuracy.
arXiv Detail & Related papers (2023-08-08T15:30:08Z) - Towards Generating Large Synthetic Phytoplankton Datasets for Efficient
Monitoring of Harmful Algal Blooms [77.25251419910205]
Harmful algal blooms (HABs) cause significant fish deaths in aquaculture farms.
Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope.
We employ Generative Adversarial Networks (GANs) to generate synthetic images.
arXiv Detail & Related papers (2022-08-03T20:15:55Z) - BronchusNet: Region and Structure Prior Embedded Representation Learning
for Bronchus Segmentation and Classification [53.53758990624962]
We propose a region and structure prior embedded framework named BronchusNet to achieve accurate bronchial analysis.
For bronchus segmentation, we propose an adaptive hard region-aware UNet that incorporates multi-level prior guidance of hard pixel-wise samples.
For the classification of bronchial branches, we propose a hybrid point-voxel graph learning module.
arXiv Detail & Related papers (2022-05-14T02:32:33Z) - SEA: Bridging the Gap Between One- and Two-stage Detector Distillation
via SEmantic-aware Alignment [76.80165589520385]
We name our method SEA (SEmantic-aware Alignment) distillation given the nature of abstracting dense fine-grained information.
It achieves new state-of-the-art results on the challenging object detection task on both one- and two-stage detectors.
arXiv Detail & Related papers (2022-03-02T04:24:05Z) - Improving Classification Model Performance on Chest X-Rays through Lung
Segmentation [63.45024974079371]
We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations.
Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
arXiv Detail & Related papers (2022-02-22T15:24:06Z) - SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to
Generate an Improved Ocean Model [72.3183990520267]
We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution.
Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties.
The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
arXiv Detail & Related papers (2021-06-24T17:29:42Z)
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.