The implementation of fleet management software in mining operations poses challenges, including high initial costs and the need for skilled personnel. Additionally, integrating new software with existing systems can be complex, requiring significant time and resources. This study aims to mitigate these challenges by leveraging advanced technologies to reduce initial costs and minimize reliance on highly trained employees. Through the integration of computer vision and multi-objective optimization, it seeks to enhance operational efficiency and optimize fleet management in open-pit mining. The objective is to optimize truck-to-excavator assignments, thereby reducing excavator idle time and deviations from production targets. A YOLO v8 model, trained on six hours of mine video footage, identifies vehicles at excavators and dump sites for real-time monitoring. Extracted data—including truck assignments and excavator ready times—is incorporated into a multi-objective binary integer programming model that aims to minimize excavator waiting times and discrepancies in target truck assignments. The epsilon-constraint method generates a Pareto frontier, illustrating trade-offs between these objectives. Integrating real-time image analysis with optimization significantly improves operational efficiency, enabling adaptive truck-excavator allocation. This study highlights the potential of advanced computer vision and optimization techniques to enhance fleet management in mining, leading to more cost-effective and data-driven decision-making.

Accurate near-real-time granulometry is critical for optimizing energy efficiency in comminution circuits, yet standard optical monitoring systems fail under the harsh conditions of industrial mining. High conveyor velocities induce motion blur, while heavy dust loads occlude particle boundaries, leading to severe under-counting of fine fractions and an overestimation of product coarseness. This paper proposes a novel tracking framework to overcome these limitations. The method integrates a five-frame temporal rolling buffer with a multi-stream augmentation architecture that decouples motion and texture features. Experimental validation on the feed of a secondary conical crusher at an operational facility demonstrates that the proposed framework recovers significant particle data lost by baseline models, correcting the measured D₈₀ and D₅₀ values of the crushed material. This correction reveals a finer true load, enabling more precise closed-loop control of crusher settings and reducing unnecessary energy consumption.