Enhanced Campus Security Target Detection Using a Refined YOLOv7 Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In many educational institutions, safety management traditionally depends upon manual video surveillance, leading to potential delays in the identification and alerting of perilous activities, notably the possession of controlled knives and smoking behaviors exhibited by students.These activities possess significant consequences for both the psychological and physical well-being of students.Recognizing this pressing need, an augmented object detection method for campus security, rooted in YOLOv7, is presented.The EIoU (Efficient Intersection over Union) loss function has been substituted to expedite model convergence and heighten detection fidelity.Additionally, the integration of the CBAM (Convolutional Block Attention Module) attention mechanism with the DCNv2 (Deformable ConvNets v2) deformable convolutional kernel not only mitigates the challenge of information inundation but also enhances feature extraction capabilities, facilitating adjustments to geometric deformations.Experimental findings indicate that this proposed method achieves a detection accuracy of 92.6% across various categories on a dataset comprising three categories, spanning a total of 4500 images, and attains an mAP of 96.4%.In comparison to the conventional YOLOv7 algorithm, enhancements in detection accuracy and mAP by 6.9% and 6.6%, respectively, have been observed, affirming the efficacy of the presented algorithm.
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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 it