AI Vision Becomes the Intelligent Guardian of Laboratories, SingTown Technology Empowers Reaction Tube Identification and Safety Inspection

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 changes in the liquid, particularly under high temperatures or in cases of splashing, 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 developed a reaction tube identification system based on SingTown'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 well in the reaction tube rack to determine whether any tubes have fallen, tilted, or broken.

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 are transmitted to the robotic arm's main control unit via serial port or bus communication, enabling control over actions such as picking up and discarding, thereby achieving intelligent sorting and safe collaboration.

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