“AI Urban Management” is here—an automatic street vending detection system built on OpenMV

Illegal street vending recurs repeatedly; the difficulty in governance lies not in “detecting it once” but in its “long-term existence.” OpenMV enables management to shift from centralized rectification to daily perception through continuous visual monitoring.

Occupying public roads for business operations is a common issue in urban governance; business facilities such as stalls, shelves, and tables encroach upon pedestrian walkways or vehicle lanes, not only disrupting traffic order but also posing safety hazards—particularly prominent during morning and evening rush hours or in densely populated areas.

Management relying on manual inspections incurs high labor costs and fails to ensure long-term, continuous supervision, easily resulting in management blind spots where problems recur immediately after personnel depart.

Based on SingTown’s OpenMV intelligent camera, automated and routine visual monitoring can be conducted in areas prone to recurring incidents, such as key road sections, school perimeters, and commercial districts.

After the camera is fixedly deployed, OpenMV runs the object detection algorithm on the edge side to automatically identify objects related to encroachment on public space, such as stalls, shelves, sunshades, and trolleys. Meanwhile, by integrating road boundaries, pedestrian passage areas, and configured virtual rule lines, it determines in real time whether an encroachment event has occurred.

When the system detects that business facilities encroach upon roads, obstruct pedestrian pathways, or exceed the scope of licensed business operations, it can instantly output identification results at the edge side for on-site alerts, violation evidence collection, or automatic reporting to the urban management platform, thereby providing objective evidence for subsequent law enforcement.

Leveraging the on-device AI and edge deployment advantages of OpenMV, the management and regulation of unauthorized street occupation have shifted from periodic rectification to continuous perception and real-time detection, significantly reducing the burden on manual inspections and enhancing both the responsiveness and precision of urban governance.