Benchmarking performance of object detection under image distortions in an uncontrolled environment
This work addresses robustness issues for object detection in real-world applications like autonomous driving, but it is incremental as it builds on existing datasets and methods.
The study tackled the problem of object detection robustness under image distortions in uncontrolled environments by evaluating state-of-the-art methods and proposing a new distortion generation strategy, resulting in a 31.5% improvement in robustness when training with the proposed dataset.
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