A model designed to identify safety hazards based on construction workers' compliance with wearing PPE during working hours and its integration of four components of PPE, its practical application for mobile usage, and a secure model against adversarial attacks are presented.
Artificial intelligence, when responsibly implemented, represents a transformative adjunct to traditional safety practices – capable of significantly improving construction site safety performance globally – but it must be deployed in tandem with organizational commitment, worker training, and robust safety cultures.
Musaed M. Al-Thubaiti, Saeed S. Al-Shahrani, Ryan A. Alsaihaty· World Journal of Advanced En...· 0 citations
Real-time detection of damage in roadside crash barriers is vital for maintaining highway safety and enabling data-driven infrastructure management. Manual inspection methods are labor-intensive and impractical over long highway stretches. This paper presents a real-time unsupervised anomaly-detection framework based on deep convolutional autoencoders for identifying damaged crash barriers from roadway imagery. The proposed system integrates YOLOv10n for crash-barrier detection and the Segment Anything Model (SAM) for precise segmentation, isolating the barrier region before analysis. The autoencoder, trained exclusively on undamaged samples, classifies anomalies by comparing reconstruction errors against an adaptive threshold. A dataset comprising over 50,000 GoProcaptured images from Indian highways was used for training and validation. Experimental results demonstrate 97% accuracy and an AUC of 0.97, confirming the framework's ability to detect dents, cracks, and deformations under varying environmental conditions. The proposed approach eliminates the need for labeled data, offering a scalable, costeffective solution for autonomous road-safety monitoring and predictive maintenance.
D. Varshney, Avnish Panwar, Indrajit Ghosh· International Conference on...· 0 citations
Damages on road surfaces like potholes and cracks influence roads safety, automobile performance, efficiency of transportation. The old fashioned road inspection systems are based on the use of manual survey, which is time consuming, expensive and not acceptable in continuous monitoring. In addition, most of the current automated systems report deterioration of roads without referring to the traffic density, therefore prioritizing maintenance inefficiently. This paper aims to resolve this drawback by introducing a proposal of an automatic road damage detection and prioritization system based on deep learning and cloud integration. YOLO object detector algorithm analyses the live camera video on the road and approximates the density of traffic, allowing high-priority roads to be identified. A Convolutional Neural Network (CNN) is then used to identify and categorize road damages i.e. potholes and cracks and determine their severity. The identified damage information and the position of the damage are sent to ThingSpeak cloud platform where they are real-time monitored and visualized with the help of the web-based dashboard. The experimental outcomes reveal that the given system can achieve proper detection, stability in real-time testing, and prioritization in maintenance issues in various environmental conditions. The system lowers the compliance cost through manually checking and it offers scalable smart road infrastructure management solution.
Kavithra I, G. S· International Conference Com...· 0 citations
Road infrastructure plays a vital role in ensuring safe and efficient transportation, making timely detection of road damage essential for reducing accidents and maintenance costs. Traditional inspection methods are often slow, expensive, and dependent on manual observation, making them unsuitable for largescale monitoring. This project presents an automated road damage detection system that uses Unmanned Aerial Vehicle (UAV) images and deep learning techniques to identify defects such as cracks and potholes. High-resolution aerial images are captured using UAVs and processed through advanced YOLO-based object detection models to accurately locate and classify damaged road sections. The collected images are preprocessed to improve detection performance, and the trained model is evaluated using standard performance metrics. The proposed system provides faster inspection, minimizes human effort, and enables continuous monitoring of road conditions over large areas. The results demonstrate that UAV-assisted deep learning offers a reliable and efficient solution for modern road infrastructure maintenance and supports timely repair planning.
Aelaboina sai sruthi, Dr.G.Purna, Chandar Rao· American Journal of AI Cyber...· 0 citations
Occupancy detection is fundamental to the operational intelligence of smart buildings, driving critical functions in energy management, HVAC automation, and physical security. While modern Deep Learning (DL) models have achieved high accuracy in parsing complex environmental sensor data, they remain highly vulnerable to adversarial examples, imperceptibly perturbed inputs designed to deceive neural networks. These vulnerabilities pose severe real-world risks, ranging from energy sabotage, in which systems heat empty rooms, to critical security breaches in which intruders go undetected. To address this security gap, we propose ADS-Guard, a novel Adversarial Detection and Sanitization (ADS) framework rooted in sequence-to-sequence autoencoder purification. Unlike standard denoising techniques, ADS-Guard incorporates a latent consistency regularization mechanism that encourages alignment between clean and adversarial representations in the latent feature space. We evaluated ADS-Guard using a comprehensive experimental pipeline comprising five distinct DL architectures (LSTM, GRU, 1D-CNN, MLP, and Transformer) across three diverse datasets: (1) The UCI Occupancy dataset (20,699 samples) for standard binary detection; (2) Building59 dataset (7200 samples) for three-class occupancy-level classification (Low, Medium, High); and (3) Room Occupancy dataset (10,129 samples), representing a highly imbalanced binary occupancy-detection task. We evaluate ADS-Guard against both Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks across diverse occupancy datasets and model architectures. We further assess the framework under adaptive white-box attacks and compare its performance with FGSM-based and PGD-based adversarial training baselines. Our results demonstrate that adversarial attacks can substantially degrade occupancy-detection performance across datasets and model architectures. ADS-Guard consistently improves robustness relative to undefended models against both FGSM and PGD attacks, recovering a substantial portion of the lost performance in binary occupancy tasks and providing meaningful gains in the more challenging multi-class setting. Furthermore, ADS-Guard remains effective under stronger adaptive threat models while providing a practical retraining-free defense that can be integrated with existing occupancy-detection systems without modifying downstream classifiers.
Pratiksha Chaudhari, Yang Xiao, Wei Sun· Italian National Conference...· 0 citations
Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning model is developed. You Only Look Once – Neural Architecture Search (YOLO-NAS) algorithm was chosen to be used in the model because of its fast and high-quality detection process. A dataset with images containing healthy workers wearing helmets & safety vests (with some workers appearing in different safety vests) is collected and used as the training set. The resulting model is then saved for future usage in making predictions with new images. Then, this model is connected to Streamlit, which provides a convenient Web-based user interface through which a user can provide an image as an input to the model. The trained neural network is applied to the input image, and all detected objects that correspond to helmet & safety vest are put in bounding boxes on the input image. Moreover, each of the bounding boxes is labeled with the name of the object and confidence score of the detection. Thus, a clear visualization of the safety equipment worn by the worker is provided. Overall, the developed detection system significantly decreases the human intervention needed for visual verification of the safety helmet or vest. The described system is a user-friendly and cost-effective way of evaluating workplace safety compliance.
S. Vijayakumar, Loganathan Nachimuthu, Balasubramaniam C et al.· 2026 International Conferenc...· 0 citations
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