JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Intelligent Systems for Tracking Field Engineers and Technicians.
Yashaswini S, Sampath Kumar S.
The identification of workers and engineers is an essential part of enhancing operational efficiency and safety standards at construction sites and other corporate settings. Using deep learning and machine learning methods, such as Convolutional Neural Networks, is the optimal strategy for real-time monitoring and analysis. This paper examines the use of CNN-based deep learning models to accurately identify and classify people in dynamic and complicated environments. These models use footage from on-site cameras to accurately identify engineers and workers from their surroundings. Among the many safety features of the system are those that track the wearing of protective apparel (PPE) and check for possible hazards, such as unsafe places or a shortage of safety gear. CNNs may be used to analyze complex structures and characteristics in images, allowing for reliable identification even under difficult circumstances like changing weather, obstructions, and dim light settings. By assuring adherence to safety methods, this artificial intelligence the system not only increases safety but also offers helpful data for enhancing workflow and resource allocation. The study shows significant potential for minimizing accidents and enhancing overall site management, underscoring the value of adopting modern machine learning tackles to produce more secure and efficient working environments.
Worksite Safety Management, Visual Computing, Machine Learning, Convolutional Neural Networks (CNN).