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Artificial Intelligence · Major Project
Wildlife Detection & Monitoring with YOLOv8
Detects species in camera-trap images and logs every sighting
2-3 weeks build
Overview
Camera traps capture thousands of frames, most of them empty. This kit trains YOLOv8 on public camera-trap images from LILA BC collections to discard empty shots and label the species in the rest. Each sighting is logged with time, camera and species, and a dashboard shows activity by hour and by location for a field researcher to review.
What makes it stand up
- 01Empty-frame filtering to cut manual review time
- 02Species detection trained on public camera-trap data
- 03Sightings log with camera, time and confidence
- 04Activity charts by species, hour and location
- 05Low-confidence frames routed to a manual review queue
Module breakdown
- 01
Dataset
Camera-trap images curated, labelled and balanced.
- 02
Detector
YOLOv8 training for chosen species classes.
- 03
Batch processor
Folder ingestion with EXIF time extraction.
- 04
Sightings store
MongoDB records per detection and camera.
- 05
Dashboard
Flask charts and a manual review queue.
After this, you will be able to
- Explain class imbalance in camera-trap data
- Defend your confidence threshold for manual review
- Demo a folder of trap images turned into a sightings log
Technology stack
- Python
- YOLOv8
- OpenCV
- Flask
- MongoDB
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support