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

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

Street vending is a common issue in urban governance, where stalls, shelves, tables, and chairs encroach upon pedestrian walkways or vehicular roads—disrupting traffic order and posing safety hazards, particularly during morning and evening rush hours or in densely populated areas.

Managing through manual inspections incurs high labour costs and makes it difficult to achieve long-term, continuous supervision, thereby creating management blind spots where problems recur 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-device to automatically identify objects related to encroachment on public space, such as stalls, shelves, sunshades, and trolleys. Meanwhile, by integrating road boundaries, pedestrian walkway extents, and configured virtual rule lines, it determines in real time whether any encroachment behaviour has occurred.

When the system detects that business facilities encroach upon roads, obstruct pedestrian pathways, or exceed the scope of their licensed business operations, 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 edge-side AI capabilities and edge deployment advantages of OpenMV, encroachment management 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.