Nighttime Detection of Illegal Crossing by Pedestrians and Pedestrian Lane Obstruction by Vehicles through Effective Deep Learning Model
Bibliographic record
Abstract
Most object detection methods can perform well in detecting pedestrians and vehicles in the daytime; however, the task becomes more difficult at night.This study measures the effectiveness of a modified Faster RCNN with a ResNet34 backbone, Squeeze and Excitation Network, Feature Pyramid Network, and Contrast Limited Adaptive Histogram Equalizer in detecting pedestrians and vehicles, and violations committed on the pedestrian lane in the Philippines setting.The results of this study show that the model showed an improvement of 15.37% from the unmodified Faster RCNN architecture and 1.31% from the Faster RCNN with a ResNet50 backbone and Feature Pyramid Network in the mean average precision metric.With the current modifications to the architecture, the model could confidently detect vehicles but had difficulty detecting pedestrians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".