A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm
Bibliographic record
Abstract
Video-based Intelligent Transportation Systems (V-ITS) can play an important role in developing a wide range of applications in transportation field. These systems use the outputs of video cameras to extract desired information by the means of various Artificial Intelligence techniques. Considering impressive advantages of applying Deep Neural Networks (DNNs) in different fields of object detection and classification, these methods have attracted a huge attention among researchers in recent years. In this regard, Convolutional Neural Networks (CNNs) as an important class of DNNs have been used for visual imagery goals in a wide variety of applications such as image recognition and video analysis, and even made their way through ITS applications. One of the most important steps of V-ITS applications is the process of vehicle detection in video frames and the high accuracy rate in this step can provide applicable data for other complementary modules such as vehicle tracking and classification. In this paper, a robust method to detect vehicles in video frames based on CNNs is proposed which provides an almost real-time performance and impressive accuracy. To overcome the challenges of building a precise vehicle detection model from still images, we have transformed the main architecture of a pre-trained ResNet-50 residual network to Faster Region-based Convolutional Neural Network (Faster R-CNN). Experimental results show that the system's sensitivity factor is 0.985 and it needs an average of 74 milliseconds to detect vehicles in real condition data. Consequently, our method can provide acceptable results in vehicle detection in terms of accuracy and execution time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".