A streamlined vehicle detection framework is demonstrated here to run a lightweight model on resource constrained devices like ESP32S3 using YOLOv5 Nano model
Vehicle detection is vital in traffic management, planning, and preventing traffic congestion and fatal accidents. This project aims to develop a comprehensive vehicle detection framework for deploying a lightweight model on resource-constrained edge devices like ESP32.
- Lightweight Model: Designed for low-power edge devices.
- Efficient Detection: Ensures accurate vehicle detection in real-time.
- Optimized Model: Fine-tuned YOLOv5 Nano with structured pruning and post-integer quantization.
- Multiple Model Variants: Best PyTorch, Pruned, Float-16, and INT8 models evaluated for performance.
- Model: YOLOv5 Nano
- Programming Languages : C, Embedded C, C++
- Firmware Development: Low-level hardware interfacing for ESP32
- Optimization: Structured pruning, post-integer quantization
- Deployment: ESP32 and other edge devices
All model variants have been thoroughly evaluated to ensure efficient detection and improved performance.
This framework is optimized to be deployed on ESP32 and other low-power edge devices, enabling real-time vehicle detection in resource-limited environments.
This project provides a streamlined, efficient, and edge-compatible vehicle detection framework, leveraging YOLOv5 Nano to enhance traffic monitoring and management.
