Computer Vision-based Bridge Detection and Control
Blockages in crusher bowls are the highest contributor to unscheduled downtime at most crushing plants. The accurate detection of the position and extent of these blockages is used to control truck tipping and primary feedrate and to initiate rock breaking. The system fully integrates with the local control system and offers open APIs for third party integration (Autonomous Rockbreaking, and Autonomous Fleet Managment).
Our cutting-edge Computer Vision Solutions seamlessly integrate with standard CCTV cameras and plant control systems, providing real-time monitoring and intelligent blockage detection. The system is designed to also interface with purpose-built Machine Vision cameras and can fuse visual data with additional sensor inputs such as depth sensors and LiDAR, delivering superior accuracy in detecting and managing material flow disruptions.
Powered by an industrial-grade, fanless AI edge controller, the system continuously monitors the static grizzly, instantly identifying blockages and relaying actionable insights to control systems. Beyond basic detection, it differentiates between spillage, overhang, and bridge formations, enabling operators to implement the most effective response strategies. This next-generation automation ensures improved efficiency, reduced downtime, and enhanced safety for mining and material processing operations.Furthermore, it can control the traffic lights so trucks do not tip on a blocked grizzly. It integrates with our Autonomous Rock Breaking software that can position the rock breaker above detected bridges autonomously as well as with our Oversize Detection and Management and Autonomous Apron Feeder Control.
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.
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.