“AI City Management” is Here: An Automated Vending-Obstruction Detection System Built on OpenMV

Illegal street vending recurs repeatedly; the challenge in governance lies not in “detecting it once” but in its “long-term persistence.” OpenMV enables continuous visual monitoring, shifting management from periodic rectification to daily awareness.

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 roadways, not only disrupting traffic order but also 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 violations resume immediately after personnel leave.

Based on SingTown Technology’s OpenMV smart 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 object detection algorithms on the edge device to automatically identify objects related to encroachment on public space, such as stalls, shelves, sunshades, and carts. Meanwhile, by integrating road boundaries, pedestrian walkway areas, and configured virtual rule lines, it determines in real time whether commercial activities have crossed designated boundaries.

When the system detects that a business facility is encroaching upon roads, obstructing pedestrian pathways, or operating beyond its licensed scope, it can instantly output identification results at the edge device 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 illegal road 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.