AI Vision Becomes the Laboratory's Intelligent Guardian, SingTown Technology Empowers Reaction Tube Identification and Safety Detection

On automated experimental production lines, robotic arms often need to perform automatic identification, positioning, picking, and placement of reagent reaction tubes. Traditional detection methods struggle to simultaneously identify the state of the reaction tubes and liquid changes, particularly under high temperatures or splashing conditions, which can easily lead to identification failure.

Therefore, laboratory automation equipment manufacturers utilise SingTown's OpenMV intelligent camera to construct a machine vision recognition system, enabling precise identification and status monitoring of reaction tubes within a 600×600×400 mm workspace, thereby ensuring the stability and safety of robotic arm operations.

Laboratory automation equipment manufacturers have constructed a reaction tube identification system based on SingTown Technology's OpenMV intelligent camera, integrating multiple technologies such as AI image recognition, machine vision positioning, and dynamic anomaly detection.

The camera is fixed approximately 400–500 mm above the robotic arm's working area. Deployed at the edge, it can capture images of the reaction tube rack in real time. Using template matching, edge recognition, and deep learning algorithms, it automatically detects each hole position in the reaction tube rack to determine whether any tubes have fallen, tilted, or cracked.

Simultaneously, through brightness variation and high-temperature characteristic recognition algorithms, the system can also detect the presence of high-temperature liquid splashes in the reaction tubes, promptly outputting alarm signals. The recognition results will be transmitted via serial port or bus to the robotic arm's main control unit, which controls actions such as picking up and discarding, enabling intelligent sorting and safe collaboration.

The entire system can operate stably under complex lighting and glass reflection conditions, with an identification accuracy rate exceeding 98%. This solution significantly enhances the intelligence level and operational safety of laboratory automation equipment.