Aug 2026· Australian Journal of Business and Social Science· Vol 1, pp. 529-557· 0 citations· 21 references
TL;DR
A five -phase validation programme covering bench metrology, public -dataset evaluation, simulator experiments, closed -track trials, and regulatory readiness against functional-safety, safety-of-the-intended-functionality, privacy, and human-machine-interface requirements is proposed.
Abstract
Driver drowsiness is a persistent road -safety problem whose episodic and under -reported nature complicates both prevention and measurement. This paper presents AI Safety Guard, a low -cost edge- AI prototype that combines non -contact facial-landmark analysis with bounded auditory and optional olfactory alerts. The proposed artefact uses local camera processing to estimate sustained eye closure, mouth opening and yawn patterns, and head -pose deviation; temporal decision fusion then triggers an active warning through a speaker or buzzer and, when enabled, a short, atomised scent pulse. Unlike cloud-dependent monitoring, the prototype is designed to retain no video and to record only minimal local event information. The study adopts a design -science and safety -by-design methodology: it reconstructs system requirements, specifies the hardware and inference architecture, formalises the tri- channel decision logic, and evaluates the credibility and limits of preliminary prototype evidence. Project documentation reports operation on Raspberry Pi-class hardware at approximately 10-15 frames per second, local event logging, hard -coded ac tuator duration, cooldown lockout, manual acknowledgement, and a scent opt-out. A website event trace reports 116 ms from a detection event to alert activation, whereas a separate pitch document claims 0.001 s actuation latency; this discrepancy is treated as an unresolved measurement issue rather than evidence of validated performance. The paper therefore distinguishes artefact feasibility from safety efficacy. It proposes a five -phase validation programme covering bench metrology, public -dataset evaluation, simulator experiments, closed -track trials, and regulatory readiness against functional-safety, safety-of-the-intended-functionality, privacy, and human -machine-interface requirements. The principal con tribution is an evidence -bounded blueprint for translating a student -developed prototype into a testable driver -monitoring system while preserving privacy and explicitly managing intervention risk. The system is not positioned as a substitute for sleep, rest, or safe pull-over behaviour, but as a supplementary warning device requiring independent validation before road deployment.
This study proposes “Vanguard AI,” an end-to-end cloud-edge architecture utilizing an experimentally validated YOLOv11m detection framework, delivering the first unified, production-viable architecture for context-aware compliance auditing and dynamic risk assessment in industrial environments.
Vishrutkumar Patel, Amol R. Madane, Srijit Maiti et al.· SN Computer Science· 0 citations
Reliable localization of partially visible humans is an essential front-end requirement for vision-based drowning-risk monitoring. However, horizontal-view water-surface surveillance remains challenging because visible human regions are often small and incomplete, while waves, foam, reflections, motion blur, and lens contamination introduce strong background interference. This paper presents AHD-YOLO, a lightweight detector based on YOLO11n for horizontal-view water-surface human-part detection. The proposed framework does not perform single-frame drowning-event recognition; instead, it detects exposed heads and hands to provide spatial observations for subsequent tracking, temporal behavior analysis, and alarm decision modules. A layer-wise hybrid downsampling strategy combines robust feature downsampling (DRFD) and Haar wavelet downsampling (HWD) to preserve salient semantic responses and high-frequency boundary details at different feature levels. In addition, an asymmetric cross-domain attention (ACA) module recalibrates multi-scale fused features to enhance target-related responses and suppress water-surface interference. A dedicated dataset containing 1,222 source images is reannotated with head and hand bounding boxes and expanded to 6,110 images using reflection, water stain, camera shake, and mixed degradations. Experimental results show that AHD-YOLO achieves 92.47% mAP50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mAP}_{50}$$\end{document} and 57.18% mAP50--95\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mAP}_{50\text {--}95}$$\end{document} with 3.06M parameters and 3.5 GFLOPs. These results demonstrate that the proposed model provides an effective accuracy–complexity trade-off for front-end perception in real-time drowning-risk monitoring.
Pedestrian-detection research and pre-crash safety assessment predominantly represent upright pedestrians, although prone, supine, lateral, seated, crouched, kneeling, partially collapsed, and fall-transition states alter target geometry, visibility, sensor signatures, and intervention time. Cross-study comparison is further limited by inconsistent posture labels, data provenance, latency boundaries, uncertainty reporting, and vehicle-response assumptions. We developed NUP-REPORT 1.0 as a provisional reporting and benchmarking framework through a structured narrative synthesis of a 45-source derivation corpus covering epidemiology, sensing benchmarks, uncertainty and assurance methods, reporting-guideline methodology, and public safety protocols. A reconstructed decision ledger documented 46 candidate concepts: 30 were retained as checklist items, 10 were assigned to an extended descriptor set, and six were merged. Each retained item was mapped to supporting evidence and classified as universal core (n = 19), component-contingent core (n = 4), or conditional (n = 7). The framework comprises six domains, a scenario-coverage matrix, five non-overlapping event timestamps, detection-referenced stopping equations, and a 30-item checklist. A purposive feasibility audit of 20 publications, including the adjacent pedestrian-detection literature not designed specifically for non-upright evaluation, illustrated checklist use. Within this sample, target orientation and static-versus-transition state were each reported explicitly in five of 20 publications (25%); none of the 17 applicable papers reported both time-to-first-detection and detection distance, none evaluated confidence calibration, and none of the 20 reported independent-unit uncertainty intervals. Vehicle-response items were non-applicable to papers making no intervention claim. These observations are sample-specific and do not estimate field-wide reporting prevalence. NUP-REPORT is not a consensus standard, certification procedure, or safety score; it is a traceable Version 1.0 proposal for study design, retrospective audit, and stakeholder refinement.
Unknown authors· Italian National Conference...· 0 citations
Real-time visual perception on resource-constrained embedded hardware must reconcile computational economy, low latency, and dependable sensing accuracy within tight power and cost envelopes. This paper reports on the Smart Navigation Device (SND), a wearable assistive perception system that performs object detection, distance ranging, sensor fusion, and speech feedback entirely on a Raspberry Pi 4B without any cloud dependency. At the core of the system is the Cascaded Detection-Ranging Fusion (CDRF) framework, a four-stage pipeline that couples the lightweight YOLO11n detector with ultrasonic time-of-flight ranging through confidence-guided detection acceptance, spatial-zone partitioning, dominant-object association, and adaptive suppression of redundant announcements. The hardware-software co-design keeps every processing stage — image capture, neural inference, ranging, fusion, and text-to-speech synthesis — local to the device, eliminating transmission latency, removing a major privacy exposure, and preserving operability where network connectivity is unreliable or absent. The framework was evaluated across 169 controlled trials spanning three obstacle categories — person, chair, and laptop — at distances from 0.30 m to 4.20 m. Detection rates of 90.4%, 92.9%, and 77.0% were obtained for the three classes respectively, with mean absolute ranging errors of 2.11 cm, 1.60 cm, and 1.79 cm. Agreement between the ultrasonic estimate and ground-truth distance was excellent (Pearson r = 0.9995, p < 10⁻²¹⁹), and Bland–Altman analysis revealed a small systematic bias of −1.10 cm (95% limits of agreement: −7.88 cm to 5.68 cm). A chi-square test indicated a statistically meaningful distance-dependent decline in laptop-class detection reliability. Benchmarked against previously reported wearable travel aids, the SND achieves comparable or better detection reliability than low-cost ultrasonic-only alternatives while additionally providing object identity, and does so at a fraction of the hardware cost and without any of the connectivity dependencies of cloud-assisted alternatives — positioning it as a reproducible, statistically grounded, and economically accessible baseline for future assistive-perception research.
S. R. Katke, Utkarsha Pacharaney· International journal of com...· 0 citations
: Road safety remains a critical global concern, with road hazards and sudden braking incidents contributing significantly to accidents. This research introduces an enhanced road safety system that combines motion sensor-based detection with computer vision algorithms to create a more comprehensive hazard alert system. The system employs smartphone sensors including accelerometers, gyroscopes, GPS, and the device camera to detect road hazards, provide lane departure warnings, and alert drivers to potential collision risks. Our application, 'RoadAware' aims to reduce accidents by offering real-time hazard alerts, mapping dangerous road conditions, and providing visual driving assistance through multiple modes including dash-mount computer vision and heads-up display reflection. This paper details the implementation of computer vision models for lane detection and forward collision warning, performance optimization techniques across various devices, and integration with existing motion-based hazard detection. Testing results demonstrate significant improvements in detection accuracy, with pothole identification reaching 93% accuracy and false positive rates for sudden braking detection reduced to 0.5%. The multi-modal approach addresses various driving contexts and environmental conditions, enhancing the system's versatility and effectiveness.
Manisha More, Aatish Bagal, Sneha Bade et al.· Proceedings of the 1st Inter...· 0 citations
Conventional closed-circuit television (CCTV) systems record crime rather than prevent it. This paper presents SmartGuard, a smart surveillance framework that combines a fine-tuned YOLOv8-nano deep learning model with an IoT alert pipeline to detect masked or disguised individuals in real time and notify residents before harm occurs. Running on a Raspberry Pi 4 edge device, the system achieves a masked-individual detection mAP50 of 0.995 with sub-1.3 second end-to-end alert latency via Firebase Cloud Messaging. A pilot survey of 32 University of East London participants returned an overall approval mean of 3.88/5. The system avoids facial recognition entirely, operating within UK GDPR constraints. Results demonstrate that effective, low-cost, privacy-conscious residential surveillance is technically feasible on affordable edge hardware.
Md Mahmudur Rahman, Mahmud Yusuf Ahmed, Kazi Tansen et al.· International Conference on...· 0 citations
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