Authors:
Reshawn Ramjattan
1
;
Rajeev Ratan
2
;
Shiva Ramoudith
3
;
Patrick Hosein
3
and
Daniele Mazzei
1
Affiliations:
1
Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy
;
2
BPP University, London, U.K.
;
3
The University of the West Indies, St. Augustine, Trinidad
Keyword(s):
Deep Learning, Continual Learning, Object Detection, Edge Computing.
Abstract:
Deep learning networks for license plate detection can produce exceptional results. However, the challenge lies in real-world use where model performance suffers when exposed to new variations and distortions of images. Rain occlusion, low lighting, glare, motion blur and varying camera quality are a few among many possible data shifts that can occur. If portable edge devices are being used then the change in location or the angle of the device also results in reduced performance. Continual learning (CL) aims to handle shifts by helping models learn from new data without forgetting old knowledge. This is particularly useful for deep learning on edge devices where resources are limited. Gdumb is a simple CL method that achieves state-of-the-art performance results. We explore the potential of using continual learning for license plate detection through experiments using an adapted Gdumb approach. Our data was collected for a license plate recognition system using edge devices and cons
ists of images split into 3 categories by quality and distance. We evaluate the application for data shifts, forward/backward transfer, accuracy and forgetting. Our results show that a CL approach under limited resources can attain results close to full retraining for our application.
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