To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather, and seasons. Continually adapting the detector to incorporate these changes is a promising solution, but it can be computationally costly. Our proposed approach is to selectively adapt the detector only when necessary, using new data that does not have the same distribution as the current training data. To this end, we investigate three popular metrics for domain gap evaluation and find that there is a correlation between the domain gap and detection accuracy. Therefore, we apply the domain gap as a criterion to decide when to adapt the detector. Our experiments show that our approach has the potential to improve the efficiency of the detector's operation in real-world scenarios, where environmental conditions change in a cyclical manner, without sacrificing the overall performance of the detector. Our code is publicly available at https://github.com/dadung/DGE-CDA.
翻译:为了确保自主系统中可靠的物体检测,检测器必须能够适应由时间、天气和季节等环境因素引起的外观变化。连续适应检测器以融合这些变化是一种有希望的解决方案,但是它可能会耗费大量计算资源。我们提出了一种选择性适应检测器的方法,仅在必要时使用新数据,这些数据的分布与当前训练数据不同。为此,我们探索了三种流行的领域差距评估方法,并发现领域差距与检测精度之间存在相关性。因此,我们将领域差距作为决定何时适应检测器的标准。我们的实验表明,我们的方法有潜力在现实场景下提高检测器的操作效率,其中环境条件以周期性的方式变化,而不会牺牲检测器的整体性能。我们的代码可在 https://github.com/dadung/DGE-CDA 上公开获取。