“AI Urban Management” is Here: An Automated Street 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.

Street vending is a common issue in urban governance, where business facilities such as stalls, shelves, and tables encroach upon pedestrian walkways or vehicular roads, adversely affecting traffic order and posing safety hazards—particularly 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, resulting in management blind spots where problems recur immediately after personnel depart.

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

After the camera is fixed and deployed, OpenMV runs the object detection algorithm at the edge to automatically identify objects related to encroachment on public space, such as stalls, shelves, sunshades, and trolleys. Meanwhile, by integrating road boundaries, pedestrian walkway areas, and configured virtual rule lines, it determines in real time whether any commercial activity has crossed the designated boundaries.

When the system detects that business facilities encroach upon roads, obstruct pedestrian pathways, or exceed their licensed scope of operations, it can instantly output identification results at the edge side for on-site alerts, evidence collection for violations, 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 of unauthorized occupation of public spaces has shifted from periodic rectification to continuous perception and real-time detection, significantly reducing the burden on manual inspections and enhancing the responsiveness and precision of urban governance.