Continual Learning Strategy in One-Stage Object Detection Framework Based on Experience Replay for Autonomous Driving Vehicle

Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continua...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-11, Vol.20 (23), p.6777
Main Authors: Shieh, Jeng-Lun, Haq, Qazi Mazhar Ul, Haq, Muhamad Amirul, Karam, Said, Chondro, Peter, Gao, De-Qin, Ruan, Shanq-Jang
Format: Article
Language:English
Subjects:
autonomous driving vehicles
Classification
continual learning
Datasets
Deep learning
Knowledge
Neural networks
one-stage object detection
Proposals
Vehicles
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Summary:Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20236777