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：为确保自动驾驶系统在目标检测中的可靠性，检测器必须能够适应由环境因素（如一天中的时间、天气和季节）引起的外观变化。持续自适应地更新检测器以纳入这些变化是一种有前景的解决方案，但可能带来较高的计算成本。本文提出的方法仅在必要时选择性地自适应检测器，即利用与当前训练数据分布不同的新数据进行自适应。为此，我们研究了三种用于评估领域间隙的常用指标，并发现领域间隙与检测精度之间存在相关性。因此，我们将领域间隙作为决定何时自适应检测器的标准。实验表明，在环境条件周期性变化的现实场景中，我们的方法能够在不牺牲检测器整体性能的前提下，提升检测器运行效率。我们的代码已公开于 https://github.com/dadung/DGE-CDA。