FasterMDE: A real-time monocular depth estimation search method that balances accuracy and speed on the edge

Monocular depth estimation (MDE) is critical in enabling intelligent autonomous systems and has received considerable attention in recent years. Achieving both low latency and high accuracy in MDE is desirable but challenging to optimize, especially on edge devices. In this paper, we present a novel approach to balancing speed and accuracy in MDE on edge devices. We introduce FasterMDE, an efficient and fast encoder-decoder network architecture that leverages a multiobjective neural architecture search method to find the optimal encoder structure for the target edge. Moreover, we incorporate a neural window fully connected CRF module into the network as the decoder, enhancing fine-grained depth prediction based on coarse depth and image features. To address the issue of bad “local minimums” in the multiobjective neural architecture search, we propose a new approach for automatically learning the weights of subobjective loss functions based on uncertainty. We also accelerate the FasterMDE model using TensorRT and implement it on a target edge device. The experimental results demonstrate that FasterMDE achieves a better balance of speed and accuracy on the KITTI and NYUv2 datasets compared to previous methods. We validate the effectiveness of the proposed method through an ablation study and verify the real-time monocular depth estimation performance of FasterMDE in realistic scenarios. On the KITTI dataset, the FasterMDE model achieves a high frame rate of 555.55 FPS with 9.1% Abs Rel on a single NVIDIA Titan RTX GPU and 14.46 FPS on the NVIDIA Jetson Xavier NX.