A deployment study of quantized YOLOv4-Tiny on Raspberry Pi 5 for aerial emergency detection, undermined by internal numeric inconsistencies and unsupported accuracy claims.
Focal loss for dense object detection,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5
A deployment study of quantized YOLOv4-Tiny on Raspberry Pi 5 for aerial emergency detection, undermined by internal numeric inconsistencies and unsupported accuracy claims.