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
翻译:物体探测算法的稳健性在现实世界应用中起着突出作用,特别是在因图像获取过程中的扭曲而导致的无控制环境中,特别是在图像获取过程中,物体探测方法的性能受到抓获扭曲的影响。在本研究中,我们提出了一个最新物体探测方法的业绩评价框架,使用专门数据集,包含不同严重程度不同扭曲的图像。此外,我们提出对MS-COCO数据集应用的原始图像扭曲生成战略,将某些地方和全球扭曲结合起来,以达到更好的性能。我们表明,使用拟议的数据集进行的培训提高了31.5 ⁇ 物体探测的可靠性。最后,我们提供了一套定制数据集,包括从MS-COCO扭曲的自然图像,以便对常见扭曲的稳性进行更可靠的评估。各种扭曲的数据库和生成源代码可以公开查阅。