Predict Equipment Maintenance with Vision AI
See how a heavy equipment operator uses vision AI to catch wear and damage before it becomes downtime.
Roboflow reads the fixed cameras, in-cab cameras, drones, and mobile equipment cameras you already have, sees the rock, the machines, the belts, and the people, and turns the mine into data, in real time and in the harshest imaging conditions in heavy industry.
Ore, Fragmentation, and Material:
Equipment, Haulage, and Maintenance:
Safety, People, and Systems Integration:
Bring intelligence to every level of the mine today.
What is computer vision in mining?
Computer vision in mining uses cameras across the pit, the plant, and the fleet, fixed cameras at crushers, belts, and access points, in-cab and equipment-mounted cameras, and drones, with deep-learning models trained for mining conditions to measure and monitor what the operation runs on: blast fragmentation and particle size, boulder and tramp metal detection, stockpile and material monitoring, equipment condition from tires to teeth, conveyor and crusher health, haul cycle tracking, and people and vehicle safety around heavy equipment. Detections and measurements flow into fleet management, dispatch, plant control, and safety systems, with imagery behind every event. Learn more about: aggregate size and gradation analysis for fragmentation and PSD, conveyor jam detection for belts, predictive maintenance for equipment condition, and PPE detection and safety zone monitoring for people around machines.
Can Vision AI handle dust, mud, vibration, and mining light conditions?
Mining is the hardest imaging environment in heavy industry, and models are trained for it rather than in spite of it. Dust behind every truck, mud on every surface in the wet season, glare off high walls at noon and floodlit benches at night, vibration on every mounted camera, and underground scenes lit only by machine lights are the actual training data when models are built on your site's footage, so the system learns what a cut tire looks like through dust rather than in a catalog photo.
Does this replace our fleet management and fatigue systems?
No. Fleet management and dispatch systems from Komatsu, Caterpillar MineStar, Modular Mining, Wenco, and Hexagon, collision avoidance systems, and cab-based fatigue monitoring do what they do well, and vision fills what they cannot see: the condition of the rock, the tires, the teeth, and the belts, the fragmentation in the tray, the person on foot outside the transponder network, and the material on the conveyor. Vision events feed those same systems, a proximity detection becomes a dispatch alert, a tire finding becomes a work order, a fragmentation measurement becomes a drill and blast input, so the mine's existing operational picture gets eyes rather than a parallel system. Roboflow supplies the detection and the record; your mine's dispatch, maintenance, and safety teams own the response.
Can it integrate with our fleet management, plant control, and safety systems?
Yes. Roboflow Inference runs on edge devices at the plant, at access points, and on equipment, and exposes a standard API and common industrial protocols, so detections, measurements, and events flow into your existing systems: fleet management and dispatch from Komatsu, Caterpillar MineStar, Modular Mining, Wenco, and Hexagon, plant control through PLCs from Allen-Bradley and Siemens and DCS platforms, CMMS and maintenance systems like SAP PM and IBM Maximo, safety and incident platforms, and site reporting, through REST, MQTT, OPC UA, discrete I/O, and direct database writes. PLC-level integration stops the belt when tramp metal is detected or holds the tipple when a person is in the zone, and every event carries site, location, equipment ID, timestamp, class, measurement, and imagery, with a full record behind every alert, work order, and shift report.
