TırGöz — Anomaly detection and route optimization

This project aims to develop a logistics monitoring and optimization platform that integrates computer vision and vehicle routing optimization to enhance efficiency in loading docks and distribution centers. The system processes live video streams from cameras to automatically detect anomalies and damages during cargo loading, estimate truck and warehouse occupancy, and perform real-time route optimization based on dynamic conditions.

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Key Objectives

1- Anomaly & Damage Detection: Detect crushed boxes, torn packaging, misplacement, and unsafe stacking through live video using deep learning models.
2- Occupancy Estimation: Estimate truck and warehouse fullness by combining object detection.
3- Dynamic Route Optimization: Optimize delivery plans using multi-depot vehicle routing with capacity and time constraints, re-planning based on real-time CV events.