From Manual Inspection to AI Detection – SingTown Technology Enables Instruments to "Read Their Own Dials

During the operation of industrial equipment, mechanical dials such as air pressure gauges and water pressure gauges are widely used to monitor the system's pressure status.

However, manual inspection is not only time-consuming and prone to errors but also difficult to achieve continuous monitoring.

To enhance equipment safety and management efficiency, instrument manufacturing enterprises have introduced SingTown's OpenMV intelligent camera recognition technology, which utilises AI machine vision and image recognition algorithms for automatic identification and analysis of dial pointers.

The system can determine in real-time whether the pressure gauge pointer is within the safe (green) zone, enabling automatic detection, anomaly alerts, and remote monitoring. It is widely applied in scenarios such as industrial pressure vessels, air compressors, and water treatment systems.

Instrument and meter manufacturing enterprises have established a dial state recognition system based on SingTown Technology's OpenMV intelligent camera recognition technology, achieving automatic detection and alarm feedback for the pointer status of pressure gauges.

The camera is positioned directly in front of the instrument, capturing live images of the pressure gauge. Through the use of computer vision and image recognition technology, it automatically identifies the pointer angle and the layout of the dial area.

The system employs colour segmentation, edge detection, and the Hough circle detection algorithm to extract the centre of the dial, the position of the scale ring, and the pointer. It then utilises an angle calculation module to determine whether the pointer is pointing to the green safety zone.

When the pointer is detected to fall within the red or yellow zone, the OpenMV module immediately outputs a high-level signal or sends an abnormal flag via the serial port, enabling automatic alarm or interlocked shutdown.

AI algorithms can carry out targeted automatic learning based on actual conditions to adapt to the colour schemes and pointer shapes of different instrument models, as well as complex environments such as varying lighting, angles, and reflections.

All recognition and judgement are completed locally on the camera end, eliminating the need for external computers. Edge AI enables real-time status recognition and control feedback.

Through this solution, enterprises have transitioned from manual inspections to AI visual automated monitoring, significantly enhancing equipment safety and supervision efficiency.