Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,
Bibliographic record
Abstract
With the progress in intelligent transportation systems in smartcities, vision-based vehicle detection is becoming an important issuein the vision-based surveillance systems. With the advent ofthe big data era, deep learning methods have been increasinglyemployed in the detection, classification, and recognition applicationsdue to their performance accuracy, however, there are stillmajor concerns regarding deployment of such methods in embeddedapplications. This paper offers an efficient process leveragingthe idea of evolutionary deep intelligence on a state-of-the-art deepneural network. Using this approach, the deep neural network isevolved towards a highly sparse set of synaptic weights and clusters.Experimental results for the task of vehicle detection demonstratethat the evolved deep neural network can achieve a substantialimprovement in architecture efficiency adapting for GPUacceleratedapplications without significant sacrifices in detectionaccuracy. The architectural efficiency of ~4X-fold and ~2X-folddecrease is obtained in synaptic weights and clusters, respectively,while the accuracy of 92.8% (drop of less than 4% compared to theoriginal network model) is achieved. Detection results and networkefficiency for the vehicular application are promising, and opensthe door to a wider range of applications in deep learning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".