论文标题
在不受控制的环境中图像扭曲下对象检测的基准测试性能
Benchmarking performance of object detection under image distortions in an uncontrolled environment
论文作者
论文摘要
对象检测算法的鲁棒性在现实世界应用中起着重要的作用，尤其是在图像采集过程中失真引起的不受控制的环境中。已经证明，对象检测方法的性能遭受了捕获畸变。在这项研究中，我们使用专用数据集为最先进的对象检测方法提出了一个绩效评估框架，该数据集包含具有不同严重程度的各种变形的图像。此外，我们提出了一种应用于MS-Coco数据集的图像失真生成的原始策略，该策略结合了一些局部和全局变形，以达到更好的性能。我们已经表明，使用拟议数据集的训练将对象检测的鲁棒性提高了31.5 \％。最后，我们提供了一个自定义数据集，其中包括从MS-Coco扭曲的自然图像，以对对常见扭曲的鲁棒性进行更可靠的评估。公开可用的数据库和不同扭曲的生成源代码
The robustness of object detection algorithms plays a prominent role in real-world applications, especially in uncontrolled environments due to distortions during image acquisition. It has been proven that the performance of object detection methods suffers from in-capture distortions. In this study, we present a performance evaluation framework for the state-of-the-art object detection methods using a dedicated dataset containing images with various distortions at different levels of severity. Furthermore, we propose an original strategy of image distortion generation applied to the MS-COCO dataset that combines some local and global distortions to reach much better performances. We have shown that training using the proposed dataset improves the robustness of object detection by 31.5\%. Finally, we provide a custom dataset including natural images distorted from MS-COCO to perform a more reliable evaluation of the robustness against common distortions. The database and the generation source codes of the different distortions are made publicly available
