Computer Vision
Automated Wildlife Detection Using Deep Learning
Published: 2026
A multimodal deep learning framework for real-time wildlife detection using RGB and thermal sensor fusion in low-light forest environments.
Problem Statement
Manual wildlife monitoring is slow, expensive, and prone to human error. Traditional camera traps fail in night or fog conditions.
Methodology
We implemented a CNN + Transformer hybrid model trained using self-supervised learning. Thermal and RGB streams are fused using feature-level attention mechanisms.
Technology Stack
PyTorchSelf-Supervised LearningMultimodal FusionEdge AI DeploymentTemporal Detection
Results & Impact
Achieved 92% detection accuracy across 18 species with 65% reduction in false positives during night monitoring.
Future Work
Integration with satellite telemetry and migration pattern prediction for ecosystem-scale intelligence.