Fish detection using Open Images Dataset and Tensorflow Object Detection

A robust fish detection model for real-time underwater fish detection in any marine environments.

Welcome to the Fish-Detection-YOLOv8 repository! This project focuses on detecting fish species using the YOLOv8 object detection algorithm. It aims to provide accurate and efficient fish detection models for applications such as marine biology research and automated fishing systems.

Fish instance segmentation using Mask-RCNN

AutoFiS: Automatic Identification of Fish Species

Fish detector using Deep learning - ResNet Model with ImageNet weights

A robust computer vision pipeline for underwater fish analysis. It uses a YOLOv8 model for stable, real-time tracking and classification. Features multiple processing modes, including buffered real-time analysis and high-accuracy offline filtering, making it a flexible tool for marine biologists and researchers.

Deep learning fish classifier combining ConvNeXt-Tiny (40 species, 98.96% accuracy) with BioCLIP-2 zero-shot recognition and AI-powered habitat mapping

This code is a PyTorch implementation of ClassAwareLoss proposed in the "Class-aware fish species recognition using deep learning for an imbalanced dataset" paper. https://www.mdpi.com/1424-8220/22/21/8268

Accessory code and meta information to the HODOR dataset

This project is a collaborative fog node-based fish detection system. It leverages fog computing to distribute image processing tasks across multiple clients and a server. The system is designed to detect fish in images and provide bounding box information using the Roboflow API.

Utilizes Deep Learning and Image Processing techniques to perform image restoration and enhancement and further apply fish / coral detection.

ORCA: Oceanic Recognition & Classification Application for sea-life analysis systems.

This project is a collaborative fog node-based fish detection system. It leverages fog computing to distribute image processing tasks across multiple clients and a server. The system is designed to detect fish in images and provide bounding box information using the Roboflow API.

Use local AI for object detection to sort and crop photos.

물고기 인식 어플

FishTrack is an autonomous, solar-powered marine monitoring buoy system that integrates sonar, edge-AI fish identification, GPS tracking, and a custom LoRa multi-hop mesh network to dynamically map marine environments and broadcast coordinates to fishers without requiring internet connectivity.

Multi-Hop LoRa Mesh Network & AI-Powered Oceanographic Monitoring Control Center. Integrates edge YOLO fish detection with BFAR MIMAROPA taxonomical database and Gemini AI verification, sonar telemetry reassembly, and a real-time Flask command dashboard.

ML app for early prediction Fish disease using Water Quality instead of images