Albumentations on GitHub in Maritime

#1

dphnAI/sonar

Large-scale LLM inference engine

#2

VIAME/VIAME

Video and Image Analytics for Multiple Environments

#3

LintaoPeng/U-shape_Transformer_for_Underwater_Image_Enhancement

U-shape Transformer for Underwater Image Enhancement

#4

zhengye1995/underwater-object-detection

Kesci underwater object detection algorithm contest

#5

milleniums/underwater-object-detection-mmdetection

和鲸社区Kesci 水下目标检测算法赛（光学图像赛项）三等奖 单模方案

#6

zml-ai/Underwater_detection

2020年全国水下机器人（湛江）大赛

#7

zzndream/ShipRSImageNet

ShipRSImageNet is the largest ship detection dataset in the Computer Vision and Earth Vision communities.

#8

LiamLian0727/USIS10K

[ICML 2024] Official repository of the paper: "Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset"

#9

Wye-Foundation/Fishial-Fish-Identification

Fish Detection (Segmentation) & Classification models and training scripts

#10

cnr-isti-vclab/TagLab

A CNN based image segmentation tool oriented to marine data analysis

#11

RDXiaoLu/SCoralDet

SCoralDet and SCoralDet Dataset

#12

zhengye1995/kesci-2021-underwater-optics

2021和鲸水下光学目标检测智能算法赛项A榜0.569 B榜0.568

#13

RDXiaoLu/MambaCoral-DiffDet

MambaCoral-DiffDet code and Dataset

#14

tnwei/waternet

Modern replication of WaterNet from "An Underwater Image Enhancement Benchmark Dataset and Beyond", IEEE TIP 2019

#15

DeepAlchemy/DCIC22_SAR_ship_detection

👁 2022年数字中国创新大赛 (DCIC 2022) 卫星应用赛题——海上船舶智能检测第三名解决方案

#16

chengshuxiao/YOLOv5-ODConvNeXt

YOLOv5-ODConvNeXt is an improved version of YOLOv5 for ship detection on drone-captured images.

#17

Jordan-Pierce/CoralNet-Toolbox

Tools for panoptic segmentation and developing machine learning models for benthic imagery

#18

BIGWangYuDong/lqit

Low Quality Image Toolbox

#19

dbofseuofhust/2020-Uodac-KG

水下目标检测算法赛 声学图像赛项 第15名代码 (15th Code for Underwater Object Detection Algorithm Competition Sonar Track )

#20

sankin97/Underwater_detection

和鲸社区Kesci 2020年全国水下机器人（湛江）大赛水下目标检测算法赛（光学）三等奖方案-单模

#21

lojzezust/WaSR

A PyTorch reimplementation of the WaSR obstacle segmentation model

#22

zwl-max/underwater-detection

2021和鲸社区Kesci 水下目标检测算法赛（湛江）（光学图像赛项）

#23

tersekmatija/eWaSR

An embedded-compute-ready maritime obstacle detection network eWaSR.

#24

dadecampo/SafeRLAUV

Safe Reinforcement Learning for Autonomous Underwater Vehicles

#25

ZZiyin/UIE-WD_Code

PyTorch implementation for "A Wavelet-based Dual-stream Network for Underwater Image Enhancement", ICASSP, 2022.

#26

tkuanlun350/Kaggle_Ship_Detection_2018

Airbus Ship Detection Challenge 10th Code.

#27

lojzezust/WaSR-T

Temporal WaSR-T model for maritime obstacle detection via semantic segmentation

#28

aidecentralized/sonar

SONAR - Self-Organizing Network of Aggregated Representations

#30

Lausen-Ng/LR-TSDet

LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images (Remote Sens. 2021)

#31

pengzhiliang/Uodac_sonar

水下目标检测算法赛（声学图像赛项） 解决方案

#32

mcgill-robotics/AUV

(Work in Progress) Software Mono-Repo of the Autonomous Underwater Vehicle "Douglas"

#33

lojzezust/SLR

Scaffolded Learning Regime for training maritime obstacle detection networks

#34

naivelogic/xview3_ship_detection

https://iuu.xview.us/challenge

#35

yuanzhengthu/LLM4UnderwaterRobot

This is the codes for “LLM-Driven Unsupervised Genetic Learning for Underwater Imaging”

#36

suhas-srinath/undive

Official repository for our paper titled "UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors"

#37

yangtiming/underwater-industrial-application-yolov5m6

This project wins the intelligent algorithm contest finalist award and stands out from over 2000teams in China Underwater Robot Professional Contest, entering the final of China Underwater Robot Professional Contest and ranking 13 out of 31 teams in finals.

#38

firekeepers/DCBD

This is the PyTorch implementation of our paper: Unsupervised underwater shipwreck detection in side-scan sonar images based on domain-adaptive techniques

#39

zhangsong1213/PDCFNet

PDCFNet: Enhancing Underwater Images through Pixel Differential Convolution and Cross-Level Feature Fusion

#40

0x0000dead/whales-identification

Библиотека для идентификации морских млекопитающих со снимков аэрофотосъемки

#41

GaneshArihanth/Vessel-Oil-Spill-Detection

Vessel Anomaly Detection is a microservices-based application for maritime surveillance. It combines a React/Vite frontend with a Node.js orchestration layer and a Python/PyTorch inference engine. Key features include real-time AIS tracking, on-the-fly oil spill segmentation via DeepLabV3+, and optimized data caching using MongoDB Atlas.

#42

Anushkaghei/Underwater-Image-dehazing

Enhancing Underwater Vision: GANs & Transformers for Dehazing and Object Detection

#43

thinkingmachines/aquafarm-segmentation

Aquaculture pond segmentation for climate smart shrimp

#44

jxwleong/coral-sleuth

Coral-Sleuth: A personal project employing deep learning techniques to sleuth out different types of corals from images. Dive in to explore the underwater world of ML!

#45

krabsng/underwater

水下图像增强

#47

CarlinJunHuang/UWM-Net--A-Mixture-Density-Network-Approach-for-Underwater-Image-Enhancement

This is the Source Code of a short paper published in IEEE CAI 2024

#48

TWSFar/UnderWater

科赛比赛

#49

veeramallirajesh/ufo-segmentation

Semantic Segmentation of Aquatic Organisms in Underwater Videos

#50

BiodataMiningGroup/unknot

Unsupervised knowledge transfer for object detection in marine environmental monitoring and exploration

#51

L-Needham/SEAING-CLEARLY

SEAING CLEARLY - A program that implements an algorithm to enhance underwater photos and videos.

#52

ada-carter/oceancvbench

OceanCVBench is a comprehensive toolkit for underwater computer vision tasks, specializing in object detection with YOLO model integration. It provides tools for data preprocessing, analysis, and model evaluation specifically designed for underwater imagery. Built by the team at oceancv.org

#53

grinvolod13/airbus-ship-detection-challange-unet

UNET model for ship segmentation from Airbus Ship Detection Challange

#54

Hritikagore11/Marine-Species-Classification-using-ConvNext

A deep learning-based image classification project using ConvNeXt-Tiny to identify 23 marine species with high accuracy. Leveraging transfer learning, data augmentation, and visualization tools to analyze performance and improve model generalization.

#55

jbalzer12/yolov1_for_ship_detection

This projects was part of my bachelor thesis in the field of Geoinformatics at the University of Münster, Germany in summer 2022.

#57

PUTvision/thermo-hardware-benchmark

The official repository of the paper Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware

#58

Siddhant1410/Underwater-Image-Enhancement-and-Restoration

A python based implementation to enhance underwater images by perorming image processing and restoration.

#60

3oskil/airbus-ship-detection

Airbus Ship Detection Challenge

#61

AdityaHegde712/Marine-Debris-Detection

A repository tracking the progress of a Marine Debris Detection project using Deep Learning.

#62

aiguo112/PanoDive360

Beyond Perspective: Latitude-Aware Crowd Counting in Equirectangular Panoramic Imagery

#64

hurdcog/sonar

Large-scale LLM inference engine

#67

LouisVraie/mooringProject

Tools for the analysis of aerial images to protect marine ecosystems on the Lérins Islands

#68

mendozajpd/Capstone-Marine-Debris

LNU 4th Year Capstone Project

#69

Naturkart-miljodir/fieldgeo

Shared code and configuration for automated NiN (Natur i Norge) habitat type mapping of Norwegian mountain/lowland/marine areas.

#76

Strongich/AirbusSegmentation

Airbus Ship Detection Challenge from Kaggle

#77

tayljordan/gender-modeling

A vision model to address bias in generalistic models when identifying women in the maritime industry.

#79

tektekgo/seewhozthere

SeeWhozThere is a self-hosted application that uses a Raspberry Pi and Google Coral to perform real-time facial recognition on RTSP camera streams. It provides a web-based dashboard to summarize daily visitors, all while keeping your data 100% local.