Bridge Detection Features

Our camera-based bridge detection systems over ROM grizzlies offer several advantages over radar-based systems. First and foremost, camera systems can provide high-resolution, real-time visual feedback, enabling more precise characterization of material properties and behavior. This is particularly useful for detecting the early stages of bridging, where granularity and texture can be important indicators. Additionally, modern computer vision algorithms can be trained to recognise various types of anomalies in the ore flow, providing more comprehensive monitoring capabilities.

In contrast, radar-based systems may struggle to differentiate between normal ore levels and potential bridging incidents, especially when the material characteristics are heterogeneous. They can also be affected by environmental conditions such as dust, humidity and temperature, requiring frequent calibration for optimal performance. Moreover, radar systems only provide distance or depth information, which may not be sufficient to analyse the nature or cause of a potential bridging scenario.

The flexibility and adaptability of camera-based systems make them highly compatible with data-driven, AI-based analytical tools. This is crucial for predictive maintenance and real-time decision-making, helping to transform mining operations into smarter, more autonomous entities. With lower costs for advanced camera hardware and the increasing efficacy of computer vision algorithms, camera-based systems offer a more versatile and cost-effective solution for modern mining operations looking to optimise their ROM bin management.

Technology

By training a machine learning model on labeled images of typical and atypical ore conditions, Minealytics can achieve a high level of granularity in real-time monitoring. For example, the algorithm could be trained to recognise and differentiate between ore, empty space, and potential bridges. When the system detects a bridge forming, it can alert operators to take corrective action immediately, thus preventing potential jams, equipment damage, or inefficiencies.

The benefit here is twofold: not only does semantic segmentation allow for real-time, automated bridge detection, but it also offers the potential for predictive analytics. By continuously analyzing the characteristics and formation patterns of the ore flow, a well-trained model can predict the likelihood of a bridge forming before it even happens. This predictive capability can be invaluable in shifting from reactive to proactive management, enhancing operational efficiency and equipment longevity. Given these advantages, semantic segmentation aligns well with the aim of transforming mining operations into more data-driven, autonomous systems.