Enhancing the Performance of Deep Learning Model Based Object Detection using Parallel Processing (Work In Progress Paper)
Bibliographic record
Abstract
The need for accelerated object detection is paramount for safety critical applications such as autonomous vehicles. This paper focuses on leveraging parallel processing techniques for enhancing the performance of object detection. Specifically, this research engineers system performance by timely detection of common objects encountered by vehicles, such as other automobiles, pedestrians, and bicycles. Deploying popular pretrained deep learning models like the You Only Look Once (YOLO) model within the Apache Spark framework, the potential enhancements in detection speed achieved through parallel processing are investigated. The capability of the system to efficiently handle large datasets and distribute time-critical applications across multiple nodes is explored to improve both latency and scalability. The one-factor-at-a-time method is used to assess the impact of different system and workload parameters on performance. Of particular interest is the impact of Spark data partitioning on performance, especially for driving scenarios where the number of objects are changing rapidly. A novel data partitioning technique that uses the principles of entropy is utilized. The overall performance objective of this research will be to improve speed for object detection in cars which can improve safety in time critical events such as sudden braking or turning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".