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

Jeng-Lun Shieh¹, Qazi Mazhar Ul Haq¹, Muhamad Amirul Haq¹

¹Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

Sensors (Basel, Switzerland)

|December 2, 2020

Summary

This study introduces a continual learning method for object detection in autonomous driving vehicles. The novel approach effectively learns new object classes while preventing catastrophic forgetting, showing minimal performance drop.

Keywords:

autonomous driving vehicles continual learning one-stage object detection

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Area of Science:

Computer Vision
Machine Learning
Autonomous Driving Systems

Background:

Object detection is crucial for autonomous driving vehicles (ADV).
ADV deployment growth necessitates detecting an expanding variety of objects.
Existing models struggle to adapt to new classes without performance degradation.

Purpose of the Study:

To propose a novel continual learning method for object detection.
To enable models to learn new object classes on the fly.
To mitigate catastrophic forgetting in continual learning scenarios.

Main Methods:

A novel continual learning method for object detection is proposed.
The method learns new object classes alongside prior knowledge.
It incorporates a cumulative memory strategy to retain previous learning.

Main Results:

The proposed ER method demonstrated a low mean Average Precision (mAP) drop of 4.3% on PASCAL VOC 2007.
This performance is competitive compared to all-classes learning.
The method achieved the lowest performance drop among existing prior art.

Conclusions:

The proposed continual learning method effectively addresses the challenge of expanding object detection classes in ADV.
It offers a robust solution to prevent catastrophic forgetting.
The method shows significant promise for enhancing the adaptability of object detection models for autonomous driving.