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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".