A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation Methods

Antoine Mauri (1, 2) , Redouane Khemmar (1, 3) , Rémi Boutteau (4, 5) , Benoit Decoux (1) , Jean Yves Ertaud (1) , Madjid Haddad (2)

1 IRSEEM - Institut de Recherche en Systèmes Electroniques Embarqués
2 Segula Technologies [France]
3 Pôle Instrumentation, Informatique et Systèmes
4 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
5 STI - LITIS - Equipe Systèmes de Transport Intelligent

In smart mobility based road navigation, object detection, depth estimation and tracking are very important tasks for improvement of the environment perception quality. In the recent years, a surge of deep-learning based depth estimation methods for monocular cameras has lead to significant progress in this field. In this paper, we propose an evaluation of state-of-the-art depth estimation algorithms based on single single input on both the KITTI dataset and the recently published NUScenes dataset. The models evaluated in this paper include an unsupervised method (Monodepth2) and a supervised method (BTS). Our work lies in the elaboration of novel depth evaluation protocols, object depth evaluation and distance ranges evaluation. We validated our new protocols on both KITTI and NUScenes datasets, allowing us to get a more comprehensive evaluation for depth estimation, especially for applications in scene understanding for both road and rail environment.

Domaines: Traitement du signal et de l'image [eess.SP]

Antoine Mauri, Redouane Khemmar, Rémi Boutteau, Benoit Decoux, Jean Yves Ertaud, et al.. A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation Methods. The 23rd IEEE International Conference on Intelligent Transportation Systems, Sep 2020, Rhodes (virtual conference), Greece. ⟨hal-02978149⟩