MADENLERDE OPERASYON YÖNETİMİNDE BİLGİSAYARLI GÖRÜ TEKNOLOJİSİNİN KULLANIMI

Data is one of the most critical decision-making tools for the success of production in mine operations. In order to maximize the efficiency of operational activities, it is critical for businesses to collect and analyze this data and then implement the necessary measures based on this data. Even though each operation has unique characteristics, it is crucial to assess how well production activities are performing. Modern technology has made automated data collection possible through many new methods, and mining operations are now utilizing these new approaches. Telemetry systems and GPS have become widely used technologies for equipment tracking and dispatching in recent years. However, the use of these systems requires OEM or retrofit devices, which requires a certain amount of hardware and extra cost. Especially in recent years, due to the widespread use of camera systems in mines, different information such as traffic, road conditions, equipment status can be extracted with cameras without the need for any extra hardware installation. Computer vision algorithms can be used to understand the data generated by the cameras used in these mining operations and to measure critical indicators for operations in real time. This paper presents a digital mine management system based on the analysis of camera data with deep learning for the monitoring and management of operational performance indicators in mining.