Self-Supervised Object Distance Estimation Using a Monocular Camera

• Main Authors: Liang, H. (Author), Ma, Z. (Author), Zhang, Q. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Cameras; 'current; Data collection; deep neural networks; Deep neural networks; Distance estimation; Estimating method; Monocular cameras; monocular distance estimation; Monocular distance estimation; Network-based; object detection; Object detection; Object distance; Object recognition; Overall networks; ShuffleNets
• Online Access: View Fulltext in Publisher

 Distance estimation using a monocular camera is one of the most classic tasks for computer vision. Current monocular distance estimating methods need a lot of data collection or they produce imprecise results. In this paper, we propose a network for both object detection and distance estimation. A network-based on ShuffleNet and YOLO is used to detect an object, and a self-supervised learning network is used to estimate distance. We calibrated the camera, and the calibrated parameters were integrated into the overall network. We also analyzed the parameter variation of the camera pose. Further, a multi-scale resolution is applied to improve estimation accuracy by enriching the expression ability of depth information. We validated the results of object detection and distance estimation on the KITTI dataset and demonstrated that our approach is efficient and accurate. Finally, we construct a dataset and conduct similar experiments to verify the generality of the network in other scenarios. The results show that our proposed methods outperform alternative approaches on object-specific distance estimation. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.