“AI城管” is here: An automated system for detecting illegal street vending, built on OpenMV

The recurring issue of street vending is not difficult to address in terms of “detecting it once,” but rather in terms of its “long-term existence.” OpenMV enables management to shift from periodic rectification campaigns to daily perception through continuous visual monitoring.

Operating businesses on public roads is a common issue in urban governance; business facilities such as stalls, shelves, and tables encroach upon sidewalks or roadways, 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 labor costs and makes it difficult to achieve long-term, continuous supervision, leading to management blind spots where violations resume immediately after inspectors 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 the object detection algorithm on the edge side to automatically identify objects related to illegal street vending, such as stalls, shelves, sunshades, and carts. Meanwhile, by integrating road boundaries, pedestrian passage areas, and configured virtual rule lines, it determines in real time whether vending activities have crossed the designated boundaries.

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 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 capabilities and edge deployment advantages of OpenMV, the management and regulation of illegal 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.