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GithubOpenMVShieldsLensRoboticsBookVideoDownloadDocsForumOpenMV.ioGithubSingTown AI Vision Technology enables more precise, steadfast, and intelligent counting on packaging production lines.
In packaging production lines, conventional infrared or mechanical counting techniques are prone to disruptions caused by lighting, velocity, and object forms, resulting in imprecise tallies or response lags.
To enhance detection accuracy and automation levels, packaging equipment manufacturers have introduced SingTown's OpenMV intelligent camera AI image recognition technology. Utilising AI machine vision and image recognition algorithms, this system automatically detects and counts packaging bags on the conveyor belt, achieving real-time signal output and coordinated control.
The packaging counting system employs SingTown Technology's OpenMV intelligent camera AI image recognition technology to monitor the quantity of packaging bags in real-time on the operational conveyor belt.
The camera is positioned 1.5–2 metres from the conveyor belt to capture images and conduct edge AI recognition of packaging bags measuring approximately 70cm × 40cm. The system employs computer vision algorithms (edge detection + motion recognition + target tracking) to ascertain the entry and exit status of the packaging. Upon detecting a complete package passing through the recognition area, it automatically generates a counting signal.
The OpenMV intelligent camera facilitates adaptable edge deployment, equipped with edge computing functionalities and diverse I/O control interfaces. It can transmit pulse signals directly to PLCs, microcontrollers, or host computers through GPIO or serial ports, enabling production synchronization and output statistics.
The system response time is below 20 milliseconds, ensuring stable adaptation to varying conveying speeds and lighting conditions.
Through SingTown Technology's OpenMV intelligent camera image recognition solution, packaging production lines have been upgraded from manual or photoelectric counting to AI visual counting, significantly enhancing detection accuracy, stability, and production efficiency.
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