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5b86813 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | Problem Statement 3 Real-Time Road Anomaly Detection from Dashcam Footage on Raspberry Pi
Objective
Build an edge AI application on Raspberry Pi that processes dashcam footage in real-time to detect and log road anomalies such as potholes and unexpected obstacles.
Project Description
Students will choose a lightweight object detector (e.g., MobileNet-SSD, YOLOv5s), convert it to an edge-optimized format (TensorFlow Lite / ONNX Runtime / ExecuTorch), and integrate it with an OpenCV video pipeline. Detected anomalies should trigger timestamped logs or saved clips.
Key Requirements
Hardware:
Raspberry Pi 5 or 4.
Raspberry Pi Camera Module v2 or USB webcam.
High-write-speed microSD card.
Where possible, aim to use the CPU without additional accelerators/hats. Solutions that are well-optimised through use of Quantisation, KleidiAI, and appropriate model selection - and therefore able to run entirely on CPU - are of great interest.
Software:
Raspberry Pi OS.
Python, OpenCV.
TensorFlow Lite / ONNX Runtime / ExecuTorch with a pre-trained, quantized detection model.
Performance Targets
≥5 FPS near-real-time inference.
High precision to reduce false positives in logging.
Robust under varying lighting conditions.
Deliverables
Source code for video processing and inference pipeline.
Optimized deployed model file (.tflite / .onnx).
Demo video with anomaly detection on sample footage.
Report on model choice, optimization and performance.
Learning Outcomes
Optimizing and deploying neural networks for edge video analytics.
Experience with embedded vision pipelines.
Understanding accuracy vs speed vs compute trade-offs on Arm platforms.
Mentoring session schedule and details
PS# Date Time Meeting Link
3 9th Feb 3:30 – 4 PM Zoom link
Meeting ID: 957 4790 4145
Passcode: 521992
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