{"id":"b3a02005-aea1-4dc8-8394-f65779b74fa3","arxiv_id":"2508.11696","paper_version":3,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":1,"one_line_summary":"A custom YOLOv8-derived model detects smoking in surveillance images with 78.90% recall and 83.70% mAP@50, running in real time on edge devices like the Jetson Xavier NX.","lead":"This paper proposes a deep learning CCTV system using YOLO models to automatically detect smoking in fire exit zones. A smart generalist might read it to see how existing AI tools can be adapted for real-time safety monitoring and regulatory compliance in public spaces.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Dataset from only 20 scenarios lacks demonstrated coverage of real CCTV variability, risking overstated generalization for the custom model.","rationale":"The reader's weakest assumption on dataset representativeness and overfitting risk is the precise load-bearing point for the central performance claim. Full text availability does not appear to add the missing diversity metrics or external validation, so the concern stands and supports shifting from UNVERDICTED to CONDITIONAL pending the proposed check.","tokens_in":1682,"tokens_out":307,"duration_ms":25445,"concrete_test":"Acquire 500 new images from 3-5 fire-exit CCTV installations outside the original 20 scenarios and evaluate the reported custom model on this OOD set; if mAP@50 falls more than 8 points below 83.70%, the generalization claim weakens.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The headline performance (recall 78.90%, mAP@50 83.70%) and claim of optimal detection across varied environments rest on the assumption that 8,124 images from 20 scenarios plus 2,708 low-light samples adequately sample the target distribution. No details are given on scenario selection, quantitative diversity (angles, densities, partial occlusions, camera heights), or confirmation that train/test splits avoid leakage. If the collected data under-represents edge cases common in actual fire-exit CCTV, the custom YOLOv8-derived model could be fitting collection-specific artifacts rather than learning robust smoking cues.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes a real-time deep learning system for detecting smoking in fire exit CCTV zones. It describes a dataset of 8,124 images collected from 20 scenarios plus 2,708 low-light samples, evaluates YOLOv8/v11/v12, introduces a custom YOLOv8-derived model with added structures, reports that the custom model achieves 78.90% recall and 83.70% mAP@50, and demonstrates inference on edge devices (Jetson Xavier NX at 52–97 ms per frame).","tokens_in":1814,"tokens_out":441,"duration_ms":28106,"significance":"If the performance claims hold under proper validation, the work could offer a deployable tool for automated safety monitoring and regulatory compliance in restricted public spaces, with practical emphasis on low-light conditions and edge-device efficiency. The empirical focus on multiple YOLO variants and hardware testing provides a concrete baseline for surveillance applications.","major_comments":[{"comment":"Dataset description: the claim that the custom model delivers 'optimal object detection across varied environments' rests on 8,124 images from only 20 scenarios plus 2,708 low-light samples, yet no quantitative metrics of scenario diversity (camera angles, densities, partial occlusions, heights) or selection criteria are supplied. This directly undermines the generalization asserted in the abstract and results.","section":"Dataset and Experiments"},{"comment":"Evaluation protocol: no details are given on train/validation/test splits, cross-validation, or leakage prevention. Without these, the headline metrics (recall 78.90%, mAP@50 83.70%) cannot be interpreted as evidence of robustness rather than possible overfitting to the collected data, which is load-bearing for the central performance claim.","section":"Results and Evaluation"}],"minor_comments":[{"comment":"Abstract: 'mAP at 50' should be written consistently as mAP@50 or mAP50 to match standard object-detection notation.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate the revisions planned for the next version.","responses":[{"response":"We agree that additional quantitative details on scenario diversity would strengthen the generalization claims. The current manuscript provides only the high-level count of 20 scenarios and low-light samples without metrics on camera angles, densities, occlusions, or heights, nor explicit selection criteria. In the revised manuscript we will add a table and accompanying text quantifying these attributes across the scenarios and describing the collection protocol and selection criteria.","revision_made":"yes","referee_comment":"[Dataset and Experiments] Dataset description: the claim that the custom model delivers 'optimal object detection across varied environments' rests on 8,124 images from only 20 scenarios plus 2,708 low-light samples, yet no quantitative metrics of scenario diversity (camera angles, densities, partial occlusions, heights) or selection criteria are supplied. This directly undermines the generalization asserted in the abstract and results."},{"response":"We acknowledge that the absence of evaluation protocol details is a limitation. The manuscript does not currently describe the train/validation/test splits, any cross-validation procedure, or leakage-prevention steps. We will revise the results section to include these specifics, stating the split ratios, confirming scenario-level separation to avoid leakage, and noting whether cross-validation was performed along with its rationale.","revision_made":"yes","referee_comment":"[Results and Evaluation] Evaluation protocol: no details are given on train/validation/test splits, cross-validation, or leakage prevention. Without these, the headline metrics (recall 78.90%, mAP@50 83.70%) cannot be interpreted as evidence of robustness rather than possible overfitting to the collected data, which is load-bearing for the central performance claim."}],"tokens_in":1338,"tokens_out":401,"duration_ms":33396,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is a practical engineering project that adapts YOLO models to spot smoking in fire exit zones on CCTV. The authors gathered 8124 images across 20 scenarios plus 2708 low-light samples, ran comparisons against YOLOv8, v11, and v12, then added structures to a YOLOv8 base. Their custom version reaches 78.9 percent recall and 83.7 percent mAP at 50, and they timed it on hardware like the Jetson Xavier NX at 52 to 97 milliseconds per inference with multithreading. That device testing is the part that stands out as useful for anyone who might actually install something like this. It moves beyond pure accuracy tables and shows the system could keep up in a real surveillance setup. The comparisons are presented plainly and the numbers are easy to read. The dataset size is reasonable for a targeted task, and the focus on a safety-critical location gives the work a clear purpose. The soft spots sit mostly with generalization. Twenty scenarios is a limited sample for claiming performance across varied environments, and without details on scenario selection, camera angles, crowd densities, or how the train and test splits were made, it is hard to rule out that the model is picking up collection-specific patterns rather than robust smoking cues. The recall under 80 percent also means some misses are likely, which matters in a setting where the goal is automatic compliance. No error analysis or failure-case breakdown is mentioned, so readers cannot easily see where the model struggles. This kind of paper is aimed at applied computer vision groups or safety-system developers who need working examples for restricted-area monitoring. Someone building similar CCTV analytics could borrow the dataset construction or the edge benchmarks. It deserves a serious referee because the experiments are concrete, the deployment angle is real, and the claims can be checked against the reported metrics even if revisions would likely push for more data diversity evidence.","headline":"This is a straightforward applied tweak of YOLOv8 for smoking detection in fire-exit CCTV, with usable edge-device numbers but thin coverage in the 20-scenario dataset.","tokens_in":2357,"tokens_out":463,"would_cite":false,"duration_ms":36904,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/RealityFromDistinction","rs_theorem":"reality_from_one_distinction","paper_passage":"custom model that retains core features of YOLOv8 while introducing enhancements for low-light conditions and varied camera angles... achieved the highest recall (78.90%)... mAP@50 score (83.70%)"},{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"dataset of 8,124 images from 20 different scenarios... augmented by rotating them, adjusting exposure, and injecting a small amount (0.1%) of noise"}],"headline":"Applied YOLO-based object detection for CCTV surveillance has no structural overlap with RS distinction-forcing or J-cost machinery.","alignment":"orthogonal","rationale":"Paper centers on dataset augmentation, custom YOLOv8-derived backbone/neck/head modifications, mAP@50/recall metrics, and edge-device latency benchmarks. RS derives spacetime, 3D, c/ℏ/G, and 8-tick periodicity from a single distinction via J-cost and φ-ladder; none of these appear in the paper's architecture, training, or evaluation.","tokens_in":43316,"confidence":"high","tokens_out":313,"duration_ms":14281,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A custom YOLO-based model detects smoking in fire exit zones with 78.9 percent recall and 83.7 percent mAP at 50.","keywords":["smoking detection","YOLO object detection","CCTV surveillance","fire safety","deep learning","edge computing","real-time monitoring"],"falsifier":"Running the custom model on a new collection of live CCTV videos from actual fire exit zones never seen during training and finding that recall drops well below 78.9 percent or mAP at 50 falls below 83.7 percent.","tokens_in":2599,"feed_emoji":"🚭","tokens_out":660,"duration_ms":65443,"temperature":0.7,"pith_summary":"This paper develops a real-time deep learning system to spot smoking in CCTV footage of fire exit zones, where it creates fire hazards. The authors test three recent YOLO models and then build a custom version from YOLOv8 with extra structures to handle low light and other surveillance difficulties. On a dataset of 8,124 images from 20 scenarios plus low-light samples, the custom model reaches 78.90 percent recall and 83.70 percent mAP at 50 while running at 52 to 97 milliseconds per inference on edge hardware. A reader would care because automatic detection could reduce human monitoring needs and help enforce safety rules in places where smoking is banned.","feed_headline":"Custom model detects smoking in fire exits at 79 percent recall","feed_subtitle":"Edge device runs live detection in 52 to 97 milliseconds to support safety rules","key_machinery":"Custom YOLOv8-derived object detection model with added structures for challenging surveillance contexts","core_discovery":"The paper claims that a custom object detection model derived from YOLOv8 with added structures for challenging surveillance contexts outperforms YOLOv8, YOLOv11, and YOLOv12. Evaluated on 8,124 images from 20 different scenarios and 2,708 low-light raw samples, the model achieves a recall of 78.90 percent and mAP at 50 of 83.70 percent. It further shows real-time suitability with inference times of 52 to 97 milliseconds on the Jetson Xavier NX, supporting deployment for public safety monitoring and regulatory compliance.","pith_inferences":["The model could connect to alarm systems that notify staff or authorities the moment smoking is detected.","Similar customizations might apply to spotting other restricted actions such as open flames or blocked exits in the same zones."],"forward_implications":["The system supports real-time automatic monitoring of smoking violations in fire exit areas.","Edge device performance enables deployment without constant cloud connectivity.","Automatic detection aids regulatory compliance by logging potential violations.","The approach provides a base for adapting similar detection to other safety rules in public spaces."],"fun_headline_variants":["Custom model spots smoking in fire exits at 79% recall","YOLO custom model tops recall at 79% for fire exit CCTV","Jetson Xavier processes smoking detection in 52-97 ms","Custom model reaches 83.7 mAP for fire exit smoking detection"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The dataset of images from 20 scenarios and low-light areas represents the full range of real-world fire exit zone CCTV conditions and lets the model generalize without overfitting.","fun_headline_variants_meta":{"raw":{"variants":["Custom model spots smoking in fire exits at 79% recall","YOLO custom model tops recall at 79% for fire exit CCTV","Jetson Xavier processes smoking detection in 52-97 ms","Custom model reaches 83.7 mAP for fire exit smoking detection"]},"model":"grok-4.3","cost_usd":0.008709,"raw_usage":{"total_tokens":3916,"prompt_tokens":650,"num_sources_used":0,"completion_tokens":73,"cost_in_usd_ticks":87087000,"prompt_tokens_details":{"text_tokens":650,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3193,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":650,"tokens_out":73,"duration_ms":31404,"temperature":1.0,"reasoning_tokens":3193,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-18T22:58:58.047897+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the custom model on a new collection of live CCTV videos from actual fire exit zones never seen during training and finding that recall drops well below 78.9 percent or mAP at 50 falls below 83.7 percent.","supporting_citations":[],"review_version":1}