Does depth estimation help object detection?

Date

2022-06-01

Author

Cetinkaya, Bedrettin
Kalkan, Sinan
Akbaş, Emre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color. However, estimated depth does not always yield improvements. Many factors affect the performance of object detection when estimated depth is used. In this paper, we comprehensively investigate these factors with detailed experiments, such as using ground-truth vs. estimated depth, effects of different state-of-the-art depth estimation networks, effects of using different indoor and outdoor RGB-D datasets as training data for depth estimation, and different architectural choices for integrating depth to the base object detector network. We propose an early concatenation strategy of depth, which yields higher mAP than previous works' while using significantly fewer parameters.

Subject Keywords

Depth estimation, Object detection, RGB-D

URI

https://hdl.handle.net/11511/97076

Journal

Image and Vision Computing

DOI

https://doi.org/10.1016/j.imavis.2022.104427

Collections

Department of Computer Engineering, Article

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Citation Formats

B. Cetinkaya, S. Kalkan, and E. Akbaş, “Does depth estimation help object detection?,” Image and Vision Computing, vol. 122, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97076.