## 4 Discussion  
This work shows a shift to long-term operational decisions that use data. Making the work of seeing data automatic helps the mining industry move to less need for people, lower costs, and a greener way of mining. The mining field can take in a lot from pairing new tech in computer vision with smart plans to push more study in AI and machine learning to deal with hard work issues. Computer vision in mining could change the sector. By means of data-driven decision-making, this approach fosters sustainable transitions and digitalization, therefore enabling the mining sector to be more efficient and future-ready. Low quality, inadequate camera counts, and visual problems resulting from dust make applying computer vision technology for processing CCTV data difficult, nevertheless. Notwithstanding these difficulties, developments in camera technology help to make these problems controllable; the running expenses to solve them are small in relation to the advantages. The YOLO framework was selected for our application due to its high real-time performance and efficiency, enabled by its single-shot architecture that provides fast and sufficiently accurate results, complemented by readily available resources, extensive community support, and comprehensive documentation, all of which enhance our research and development efficiency and confirm YOLO as the optimal choice. The video recordings, obtained from the mining operation as agreed at the project's outset and not publicly available, were processed at one frame per second--analyzing only one out of every 30 frames from a 30 FPS camera feed--since the equipment's status changes moderately slowly, allowing us to efficiently utilize computational capacity while maintaining effective real-time algorithm execution. A significant limitation of the proposed method for optimizing truck-to-excavator assignments lies in the challenge of ensuring human drivers adhere precisely to algorithm-driven schedules in real-world operations. Although the study showcases the theoretical potential of computer vision as an alternative to real-time dispatching systems like DISPATCH, its practical application is constrained by the complexities of coordinating human behavior, suggesting that optimal performance may require automation. As a  
Figure 10: Truck to excavator assignment of a 10 h shift. potential direction for future research, integrating the system with a mobile application installed on drivers' smartphones could provide real-time guidance and feedback, offering a practical interim solution. The algorithm was primarily tested during the day under clear weather conditions, with limited testing conducted at night. It was observed that the model performs effectively in well-lit areas but may face challenges in low-visibility conditions. Similarly, in foggy weather, the detection accuracy depends on whether trucks and excavators remain within the camera's field of vision; otherwise, the model fails to recognize objects. Another limitation is that cameras in the loading area must not only cover the loading zone but also capture truck entry and exit points to ensure accurate tracking. Additionally, equipment failures require manual deactivation and reactivation in the system, which may cause delays in operations. To enhance the system, future developments could integrate automated malfunction detection based on prolonged inactivity or visual cues such as smoke, enabling immediate reporting and notification of maintenance teams. Furthermore, incorporating natural language processing models could enhance real-time status detection of equipment, improving overall efficiency in monitoring activities such as loading, dumping, and maneuvering. Further studies are needed to evaluate the viability and impact of this approach, potentially facilitating a transition toward fully automated vehicle fleets. Generally, the use of computer vision in mining supports sustainable practices and digitization for a more effective future in the sector by means of data-driven decision-making.