From "Seeing" to "Understanding," SingTown Technology Drives Visual Advancement

In intelligent service robot applications, environmental perception and target recognition are key technologies for achieving autonomous interaction and task execution.

The robot needs to recognize multiple objects in complex indoor environments, such as room doors and their open/closed status, human gestures, QR codes, and typical objects in daily scenarios (e.g., tabletops, items, markers, etc.). To achieve this, service robot manufacturers plan to adopt SingTown Technology's OpenMV smart camera to build a multi-object recognition system based on machine vision. The system is installed on the robot's head or body, capable of capturing images in specified directions in real-time according to master control instructions, identifying target categories, dimensions, and color features, and returning the recognition results to the host for path decision-making, task triggering, or voice interaction responses. Additionally, it can be extended to recognize QR codes for navigation positioning, task identification, or authentication, enabling robots to achieve higher-level intelligent behaviors in areas such as daily life, security, and guided tours.

Service robot manufacturers have developed a multi-target recognition system based on SingTown's OpenMV intelligent camera, enabling service robots to achieve intelligent visual perception in complex scenarios. This system integrates computer vision, AI image recognition, template matching, color tracking, morphological analysis, and QR code decoding algorithms.

The system captures real-time images of the robot's forward field of view through the camera. The edge-side AI utilizes an object detection model to identify object edges and shape features, enabling the determination of door positions and their open or closed status. Through color segmentation and brightness variation analysis, it can recognize the colors, dimensions, and relative distances of various objects.

This robot can also recognize gestures. Under close-range conditions, the system, based on contour tracking and key point matching algorithms, can identify common actions (such as raising a hand, pointing, waving, etc.). Additionally, the built-in QR code recognition function enables the robot to identify navigation points or receive task instructions.

This system enables edge deployment, with its algorithm operating efficiently in embedded environments, achieving a frame rate of over 20fps and a recognition latency below 100ms.

The system can communicate with the main control microcontroller via serial port, I²C, or CAN bus, outputting recognition categories, dimensions, colors, and status information to achieve intelligent linkage with the host.

Test results indicate that the solution developed based on the SingTown OpenMV intelligent camera achieves an indoor recognition accuracy rate of over 92% under varying lighting conditions. It supports multiple task scenarios, such as home companion robots, security patrol robots, and guide robots, significantly enhancing the robot's environmental understanding and interaction capabilities.