Joint Motion Correction and 3D Segmentation With Graph-Assisted Neural Networks For Retinal Oct

The robustness of object detection algorithms plays a prominent role in real-world applications, especially in the uncontrolled environments due to distortions during image acquisition. It has been proved that object detection methods suffer from in-capture distortions to perform a reliable detection. in this study, we present a performance evaluation framework of the state-of-the-art object detection methods on 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 a better realism. We have shown that training with this distorted dataset improves the robustness of models by 31.5%. Finally, we provided a custom dataset including the natural images distorted from MS-COCO to perform a more relevant evaluation of the robustness concerning distortions. The database and the generation source codes of the different distortions are publicly available.