●82 reads●MIT License
Comparing YOLOv8, SSD, and Faster-RCNN for Real-Time Object Detection
Table of contents
Comparing YOLOv8, SSD, and Faster-RCNN for Real-Time Object Detection
Abstract
This project evaluates three leading object detection models—YOLOv8, SSD, and Faster-RCNN—on their ability to perform real-time detection for applications like security, robotics, and autonomous systems. By analyzing their performance across metrics such as mAP50, inference time, and FPS, we provide insights into the trade-offs between speed and accuracy.
Table of Contents
- Introduction
- Technical Approach
- Key Results: Comparison of Detection Models
- Visual Results
- Conclusion
Introduction
In the field of computer vision, real-time object detection is a critical challenge. This project compares three popular models:
- YOLOv8: Known for its speed and efficiency, making it ideal for resource-constrained environments.
- SSD: Offers a balanced approach between speed and precision, suitable for general applications.
- Faster-RCNN: Excels in accuracy but is computationally intensive, best for high-precision tasks.
The goal of this project is to evaluate these models in real-world scenarios and provide actionable insights for developers.
Technical Approach
Dataset
- COCO128: A subset of the COCO dataset, pre-processed for training and testing object detection models.
Frameworks & Tools
- YOLOv8 (Ultralytics): For model training and inference.
- TensorFlow & OpenCV: For additional processing and benchmarking.
- Jupyter Notebook: For experimentation and visualization.
Metrics Evaluated
- mAP50 (%): Mean Average Precision at 50% Intersection-over-Union (IoU).
- Inference Time (ms): Time taken to process an image.
- FPS: Frames per second for real-time performance.
Key Results: Comparison of Detection Models
| Model | mAP50 (%) | Inference Time (ms) | FPS |
|---|---|---|---|
| YOLOv8 | 🟢 85.6 | 🟢 10.3 | 🟢 97 |
| SSD | 78.2 | 16.5 | 60 |
| Faster-RCNN | 89.3 | 120.4 | 8 |
Insights:
- YOLOv8 demonstrated exceptional real-time performance, making it ideal for low-latency systems.
- Faster-RCNN achieved the highest accuracy, making it suitable for precision-critical tasks.
- SSD struck a balance between speed and accuracy, making it versatile for general use cases.
Visual Results
1. YOLOv8 Detections
YOLOv8 successfully detects objects in real-time, demonstrating its precision and speed.
2. Real-Time Detection Video
Watch the YOLOv8 Real-Time Detection Video on Vimeo
3. Comparison between YOLOv8, SSD, and Faster-RCNN
Below are visual comparisons of the models' performance metrics and detections:
- Detection Examples:
4. TensorBoard Metrics
Key metrics observed during training:
- Training Loss vs. Epochs
- mAP Trends Over Epochs
Conclusion
This project highlights the trade-offs between speed and accuracy in object detection models:
- YOLOv8: The best choice for real-time applications due to its speed and efficiency.
- Faster-RCNN: Ideal for tasks requiring high precision, despite its slower performance.
- SSD: A balanced model for scenarios that require a mix of speed and accuracy.
Future work will extend this project to include object tracking and advanced analytics like facial emotion detection, further broadening its real-world applications.
What do you think?
Feel free to explore the project, share your feedback, or suggest future improvements!
How to Reproduce This Work
- Access the full code and datasets here:
GitHub Repository