Robust Firearm Detection in Low-Light Surveillance Conditions Using YOLOv11 with Image Enhancement
Bibliographic record
Abstract
In modern surveillance systems, real-time detection of security threats such as firearms in low-light environments remains a significant challenge.This study presents a robust firearm detection framework based on the YOLOv11 object detection model, enhanced with a three-stage image pre-processing pipeline tailored for dark conditions.The proposed system integrates adaptive gamma correction, Gaussian noise reduction, and min-max normalization to improve visual clarity before detection.Images from publicly available datasets were synthetically darkened to simulate real-world low-light scenarios.A custom dataset with 3,107 images was used to train and evaluate the model.The enhanced YOLOv11 model achieved a detection accuracy of 97.28%, with a mean F1score of 95.78%, significantly outperforming the baseline YOLOv11 under dark conditions.This study demonstrates that strategic image enhancement improves detection robustness and reduces false positives and false negatives in low-light surveillance applications.
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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 | 0.000 |
| 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".