“AI Urban Management” Is Here: An Automatic Unauthorized Vending Detection System Built on OpenMV

Illegal street vending recurs repeatedly; the challenge of governance lies not in “detecting it once” but in its “long-term existence.” OpenMV enables management to shift from concentrated 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 sidewalks or vehicular lanes, not only disrupting traffic order but also posing safety hazards—particularly prominent during morning and evening rush hours or in densely populated areas.

Managing through manual inspections incurs high labor costs and makes it difficult to achieve long-term, continuous supervision, easily resulting in management blind spots where violations resume immediately after inspectors leave.

Based on SingTown’s OpenMV smart camera, automated and routine visual monitoring can be conducted in key sections, areas surrounding schools, commercial districts, and other locations prone to recurring incidents.

After the camera is fixedly deployed, OpenMV runs object detection algorithms on the edge device 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 any business operation has crossed the designated boundaries.

When the system detects that business facilities encroach upon roads, obstruct pedestrian pathways, or exceed licensed business scopes, 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 road occupation have shifted from periodic rectification to continuous perception and real-time detection, significantly reducing the burden on manual inspections and enhancing the responsiveness and refinement of urban governance.