Souare, Mamady CheickToy, IbrahimYusefi, AbdullahDurdu, Akif2025-10-102025-10-1020259798331514822https://doi.org/10.1109/ISAS66241.2025.11101892https://hdl.handle.net/20.500.13091/10873Worker safety is a critical concern in industrial and construction environments, where hazardous conditions can pose significant risks to employees. Ensuring that workers wear appropriate safety equipment, such as safety helmets and vests, is essential in preventing serious workplace injuries and illnesses. However, traditional monitoring methods may be insufficient for effectively detecting whether workers are adhering to safety regulations. Manual inspections, while common, can be time-consuming, and difficult to implement consistently across large worksites. This paper explores the application of the You Only Look Once object detection algorithm to automatically detect safety helmets and vests in real-time. By combining deep learning and computer vision methods, the implemented solution aims to enhance workplace safety compliance by providing an efficient, scalable, and accurate method for monitoring workers. The real-time nature of YOLO enables swift identification of safety violations, allowing for prompt corrective actions. This approach has the potential to significantly improve worker protection while reducing the reliance on manual inspection processes, ultimately contributing to a safer and more efficient working environment. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessComputer VisionDeep LearningSafety HelmetSafety VestWorker SafetyYOLOAccident PreventionLearning SystemsObject DetectionObject RecognitionOccupational RisksSafety DevicesConstruction EnvironmentDeep LearningIndustrial EnvironmentsManual InspectionReal-TimeSafety HelmetSafety VestWorker SafetyWorkers'YOLOComputer VisionConference Object10.1109/ISAS66241.2025.111018922-s2.0-105014926220