Lightweight Monocular Depth Estimation on Edge Devices
Bibliographic record
Abstract
Given monocular images as inputs, monocular depth estimation (MDE) infers pixel-level depth. MDE is always a critical stage in scene sensing on edge devices. Existing MDE studies frequently employ deep neural networks (DNNs) for MDE, but they still face some problems, such as sacrificing computational complexity and efficiency in return for great precision, or losing more precision in exchange for increased efficiency. To alleviate these issues; 1) we propose an encoder–decoder network (EdgeNet) for precise and fast MDE on different edge devices. When recovering depth in the decoder, we design upsampling modules to aggregate global depth information with low computational complexity, improving the accuracy of the decoder by extracting its different ranges of depth information; 2) we develop a two-stage channel pruning method to, respectively, prune the encoder and decoder based on their characteristics. Our pruning method further reduces latency and model/computational complexity of EdgeNet, while losing little accuracy; and 3) we optimize the pruned EdgeNet to decrease graphics processing unit (GPU) scheduling overhead. The optimization accelerates MDE inference by an order of magnitude on the TX2 GPU device, when the input resolution is 224$\times $224. Extensive experiments show that our strategies are effective on different edge GPU devices, when input resolutions differ in outdoor or indoor scenes. For example, compared with the state of the art, the optimized EdgeNet, respectively, reduces the GPU latency by 76.3% and 89.2% on Nano and TX2 GPU devices with 2.6% lower root mean square error when the input resolution is 128$\times $416.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".