Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Bibliographic record
Abstract
Object detection is considered one of the most challenging problemsin this field of computer vision, as it involves the combinationof object classification and object localization within a scene. Recently,deep neural networks (DNNs) have been demonstrated toachieve superior object detection performance compared to otherapproaches, with YOLOv2 (an improved You Only Look Once model)being one of the state-of-the-art in DNN-based object detectionmethods in terms of both speed and accuracy. Although YOLOv2can achieve real-time performance on a powerful GPU, it still remainsvery challenging for leveraging this approach for real-timeobject detection in video on embedded computing devices withlimited computational power and limited memory. In this paper,we propose a new framework called Fast YOLO, a fast You OnlyLook Once framework which accelerates YOLOv2 to be able toperform object detection in video on embedded devices in a realtimemanner. First, we leverage the evolutionary deep intelligenceframework to evolve the YOLOv2 network architecture and producean optimized architecture (referred to as O-YOLOv2 here) that has2.8X fewer parameters with just a 2% IOU drop. To further reducepower consumption on embedded devices while maintaining performance,a motion-adaptive inference method is introduced intothe proposed Fast YOLO framework to reduce the frequency ofdeep inference with O-YOLOv2 based on temporal motion characteristics.Experimental results show that the proposed Fast YOLOframework can reduce the number of deep inferences by an averageof 38.13%, and an average speedup of 3.3X for objectiondetection in video compared to the original YOLOv2, leading FastYOLO to run an average of 18FPS on a Nvidia Jetson TX1 embeddedsystem.
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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 | 0.002 | 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".