Real-time object detection running on Raspberry Pi
Successfully deployed YOLO-based object detection on the Raspberry Pi camera feed with real-time inference and performance validation.
We've successfully implemented real-time object detection on the Raspberry Pi 5 using the camera module. Here's what we accomplished:
1. Configured YOLO model for efficient inference on RPi hardware. 2. Optimized camera feed pipeline for low-latency processing. 3. Achieved real-time object detection with acceptable FPS performance. 4. Validated detection accuracy across various test scenarios. 5. Benchmarked inference speed and resource utilization. 6. Tested multiple object classes and detection confidence thresholds. 7. Documented performance metrics and edge cases.
The object detection pipeline is now operational and ready for integration with motor control systems. Next steps involve connecting the ESC and servos for autonomous driving capabilities.
Roadblocks faced
Encountered significant package conflicts between numpy and ultralytics versions on the Raspberry Pi. Resolution required careful dependency management and virtual environment isolation to ensure compatibility across YOLO, OpenCV, and Pi-specific libraries.
Photos
Object detection inference running on Raspberry Pi
Real-time detection results with bounding boxes
Videos
Live object detection demo
Multi-object detection test