Radar Classifier Reveals the Cost of Sparse Detections
💡A simple radar MLP doubles F1 with more detections—revealing where sparse sensing breaks classification.
⚡ 30-Second TL;DR
What Changed
The classifier distinguishes cars, large vehicles, two-wheelers, pedestrians, and pedestrian groups with a histogram-based MLP.
Why It Matters
The findings show that apparent radar-classification improvements may depend more on observation density and data splitting than on model size. Autonomous-driving teams should treat single-scan classification as a sparse-data problem and evaluate performance by detection count and object class.
What To Do Next
Add multi-scan accumulation and a PointNet baseline, then report Macro F1 separately for each detection-count bucket and object class.
Key Points
- •The classifier distinguishes cars, large vehicles, two-wheelers, pedestrians, and pedestrian groups with a histogram-based MLP.
- •Macro F1 varies strongly with detection density, rising from 0.381 for one detection to 0.764 for five detections.
- •Two-wheelers are frequently confused with pedestrians because stationary or idling objects have overlapping velocity-compensated distributions.
- •Train-validation-test split choice caused more performance variation than larger MLPs, alternative encodings, or histogram binning.
- •Future work includes PointNet-style spatial encoding, multi-scan accumulation, and micro-Doppler features.
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Original source: Reddit r/MachineLearning ↗
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