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Instance Segmentation Model Evaluation and Rapid Deployment for Autonomous Driving Using Domain Differences
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DOI：10.1109/TITS.2023.3236626.png)
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Abstract
En 中文
This paper studies the model evaluation and rapid deployment problems when the model trained by the source domain is deployed to multiple other target domains in autonomous driving application scenarios. To address the problem that the existing methods for evaluating model accuracy require complete labeled test sets, this paper proposes an instance segmentation model evaluation method based on domain differences, which can give the prediction accuracy of the model on unlabeled test sets. Furthermore, to address the rapid model deployment, this paper proposes a calculation method for the minimum number of supplements based on domain differences. The method selects the fewest images from the target domain scene and realizes the rapid deployment of the model through further annotation and training. Finally, experiments are conducted on the public dataset Cityscapes. The model evaluation experiments show that the root mean square error (RMSE) of the proposed method on Cityscapes is about 4% smaller (from 6% to 2%) than that of other existing model evaluation methods, and it can be as small as 2.35%. Rapid deployment experiments show that the RMSE of the proposed method on Cityscapes is about 8%.
Keywords:
Autonomous vehicles
Image segmentation
Adaptation models
Predictive models
Data models
Training
Task analysis
Autonomous driving
instance segmentation
model evaluation
model deployment
domain difference
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