Deep Learning | Joey's Personal Tech Website

Biodiversity Monitoring System

Mon, 15 Dec 2025 00:00:00 +0000

AI-powered biodiversity monitoring platform using camera traps and video analysis to automatically detect and classify wildlife including animals, birds, fish, and plants.

Background

Biodiversity monitoring is essential for ecosystem conservation, environmental impact assessment, and wildlife management. Traditional manual observation methods are labor-intensive, time-consuming, and limited in coverage. This project leverages advanced computer vision and deep learning technologies to build an automated biodiversity monitoring system that can identify and track wildlife species 24/7 across remote locations with minimal human intervention.

The system addresses key challenges in ecological research: species identification accuracy, real-time monitoring capability, large-scale data processing, and integration of multi-source sensor data from distributed camera networks.

Technical Architecture

Hardware System

Camera Traps: Motion-triggered wildlife cameras with infrared night vision
Underwater Cameras: Waterproof cameras for fish and aquatic species monitoring
High-Resolution IP Cameras: For continuous video streaming in key habitats
Edge Computing Devices: NVIDIA Jetson for on-site AI inference
Solar Power Systems: Off-grid power supply for remote deployments
LoRa/4G Communication: Wireless data transmission from field devices

Software Stack

Image/Video Capture: OpenCV + GStreamer for multi-source input
Object Detection: YOLOv8/YOLOv9 for real-time species detection
Species Classification: ResNet/EfficientNet fine-tuned models
Tracking Algorithm: DeepSORT for multi-object tracking
Data Pipeline: Apache Kafka for stream processing
Backend API: FastAPI + PostgreSQL + MinIO
Visualization Dashboard: React + ECharts for data analytics

Core Features

Multi-Species Detection

Wildlife Detection: Automatic detection of mammals, reptiles, and amphibians
Bird Recognition: Species identification with behavior classification (flying, perching, feeding)
Fish Monitoring: Underwater species detection and population counting
Plant Identification: Flora species recognition and health assessment

Intelligent Analysis

Population Estimation: Statistical analysis of species abundance
Activity Pattern Analysis: Temporal distribution of wildlife activities
Behavior Recognition: Feeding, mating, migration behavior detection
Anomaly Detection: Alerts for invasive species or abnormal patterns

Data Management

Real-time Streaming: Live video analysis with instant notifications
Historical Archive: Searchable database with species tags and timestamps
Export & Reporting: Automated biodiversity reports and data exports
API Integration: RESTful API for third-party system integration

Results

Detection accuracy exceeds 92% across 200+ species
Processing capability of 100+ concurrent video streams
80% reduction in manual species identification workload
Successfully deployed in 15+ conservation sites
Over 500,000 wildlife observations recorded and classified

Water Quality AI Recognition System

Mon, 10 Nov 2025 00:00:00 +0000

AI-powered water quality analysis system using industrial cameras for SV30 sludge settling ratio and floc morphology recognition.

Background

In wastewater treatment and water purification processes, SV30 (sludge settling ratio) and floc morphology are critical indicators for evaluating treatment effectiveness. Traditional manual observation methods suffer from low efficiency, high subjectivity, and inability to provide 24/7 monitoring. This project uses industrial cameras for real-time image/video capture, combined with OpenCV image processing and deep learning technology, to achieve automated intelligent recognition and analysis of water quality indicators.

Technical Architecture

Hardware System

High-precision industrial cameras (GigE/USB3.0 protocol support)
Programmable LED ring light for stable lighting conditions
Edge computing devices (NVIDIA Jetson / Industrial PC)
Standardized sampling containers and mounting systems

Software Stack

Image Capture: OpenCV + Industrial Camera SDK
Image Preprocessing: Noise reduction, enhancement, ROI extraction, perspective correction
SV30 Recognition: Edge detection + liquid surface segmentation algorithms
Floc Analysis: YOLO/U-Net deep learning models
Data Storage: Time-series database + image archival
Visualization: Real-time web dashboard

Core Features

SV30 Settling Ratio Recognition

Real-time tracking of sludge settling process
Automatic identification of sludge-water interface
Precise calculation of 30-minute settling ratio
Generation of settling curves and trend analysis

Floc Morphology Recognition

Floc size distribution statistics
Floc density and uniformity analysis
Real-time flocculation effectiveness evaluation
Automatic alerts for abnormal floc conditions

Intelligent Analysis & Alerts

Historical data comparison analysis
Water quality trend prediction
Automatic alerts for abnormal conditions
Dosing optimization recommendations

Results

Recognition accuracy over 95%
24/7 unattended monitoring capability
60% reduction in manual inspection costs
15% savings in chemical dosing costs through optimization

AI Intelligent Chatbot

Mon, 10 Mar 2025 00:00:00 +0000

Intelligent conversational system based on large language models, supporting multi-turn dialogue, knowledge base Q&A, and intent recognition.

Highlights

Integration with mainstream LLMs (GPT/Claude/Qwen)
RAG (Retrieval-Augmented Generation)
Enterprise knowledge base Q&A
Intent recognition and slot filling
Multi-channel access (Web/App/WeChat)

Computer Vision Recognition System

Wed, 28 Feb 2024 00:00:00 +0000

Deep learning-based image recognition and object detection system.

Highlights

Model training with PyTorch/TensorFlow
Object detection (YOLO/Faster R-CNN)
Image classification and recognition
OCR text recognition
Edge device deployment optimization