Scene Understanding for Autonomous Driving
This is an incremental study applying existing methods to autonomous driving datasets, with potential benefits for researchers and practitioners in that domain.
The paper evaluated different configurations of RetinaNet, Faster R-CNN, and Mask R-CNN from Detectron2 on KITTI-MOTS and MOTSChallenge datasets for object detection and segmentation in autonomous driving, finding significant performance improvements after fine-tuning and hyperparameter optimization.
To detect and segment objects in images based on their content is one of the most active topics in the field of computer vision. Nowadays, this problem can be addressed using Deep Learning architectures such as Faster R-CNN or YOLO, among others. In this paper, we study the behaviour of different configurations of RetinaNet, Faster R-CNN and Mask R-CNN presented in Detectron2. First, we evaluate qualitatively and quantitatively (AP) the performance of the pre-trained models on KITTI-MOTS and MOTSChallenge datasets. We observe a significant improvement in performance after fine-tuning these models on the datasets of interest and optimizing hyperparameters. Finally, we run inference in unusual situations using out of context datasets, and present interesting results that help us understanding better the networks.