This study explores human-robot interaction (HRI) based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace. Using object segmentation, we propose an approach that is capable of analyzing these situations in real-time and providing useful information to avoid critical working situations. In the industry, ensuring the safety of workers is paramount, and solutions based on robots and AI can provide a safer environment. For that, we proposed a methodology evaluated with two different YOLO versions (YOLOv8 and YOLOv5) alongside a LoCoBot robot for supervision and to perform the interaction with a user. We show that our proposed approach is capable of navigating a test scenario and issuing alerts via Text-to-Speech when dangerous situations are faced, such as when hardhats and safety vests are not detected. Based on the results gathered, we can conclude that our system is capable of detecting and informing risk situations such as helmet/no helmet and safety vest/no safety vest situations.

In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwears. The recommended approach uses the YOLO v7 (You Only Look Once) object detection algorithm to precisely locate these safety items. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a mAP@0.5 score of 87.7\%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research makes a contribution to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry

When cars were invented, horses were replaced by engines; saddles were replaced by seats; and reins were replaced by a steering wheel and foot pedals. There were no seat belts, safety bumpers, anti-lock brakes or other safety equipment. The Model T had a top speed of 40-45 miles per hour, which -- considering the lack of safety equipment -- was pretty fast. That was more than 100 years ago. Technology has progressed through the years, from seat belts to safety bumpers, to anti-lock brakes, airbags and a host of other safety features.

Artificial intelligence is described as having machines do "smart" or "intelligent" matters on their very own barring human guidance. AI security entails leveraging AI to become conscious of and provides up cyber threats with much less human intervention than is usually predicted or wished with normal protection approaches. AI safety equipment are regularly used to pick out "good" versus "bad" with the aid of evaluating the behaviours of entities throughout surroundings to these in a comparable environment. This system allows the gadget to mechanically examine about and flag changes. Often known as unsupervised gaining knowledge of or "pattern of life" learning, this technique effects in giant numbers of false positives and negatives.