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Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Wang, Huai-Mu Lin, Huei-Yung Chang, Chin-Chen |
| Copyright Year | 2021 |
| Description | In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods. |
| Starting Page | 4755 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21144755 |
| Journal | Sensors |
| Issue Number | 14 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-12 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Object Detection Depth Estimation Stereo Vision Deep Learning |
| Content Type | Text |
| Resource Type | Article |
