AI Pothole & Garbage Detection — One Platform for Smart Cities & Campuses

An AI pothole detection system uses computer vision on CCTV or vehicle-mounted camera feeds to automatically detect road damage, classify its severity, and record its GPS location in real time. Instead of waiting for accidents or citizen complaints, it continuously surveys the road network and sends geo-tagged alerts to maintenance teams. Fixed cameras cover known black spots while cameras on patrol or garbage vehicles survey the whole network during normal routes.

AI models analyse camera feeds to recognise overflowing bins, scattered waste accumulation, and illegal dumping, then send a geo-tagged alert to the sanitation team. The same system can detect the act of littering or dumping and capture timestamped video evidence, optionally reading the offending vehicle's number plate via ANPR for enforcement. It runs on existing CCTV, so there is no need to install sensors in every bin.

Yes, and monsoon is precisely when it matters most, because potholes form fastest and are often hidden under water. The system runs a monsoon monitoring mode with heightened sensitivity, including detection of partially water-filled potholes that drivers cannot see, and it reassesses road condition after each spell of rain. It also tracks June to September deterioration year over year so cities can compare monsoon damage across seasons.

Yes. Tentovision is vendor-agnostic over ONVIF and RTSP, so it works with the junction, traffic, building, and campus cameras already installed — Hikvision, Dahua, CP Plus, Axis, and others — without rip-and-replace. It also supports cameras mounted on vehicles and ordinary dashcams. Using existing infrastructure is what keeps deployment affordable.

Yes. The same platform monitors internal roads and waste bins across IT parks, residential townships, malls, hospitals, and industrial estates using their existing CCTV. Facility managers get automated pothole and overflow alerts, a cleanliness score per zone, and SLA proof against housekeeping or waste contractors — replacing daily manual walkthroughs, and supporting LEED/IGBC cleanliness documentation where required.

IoT fill-level sensors give a precise fill percentage but only for the single bin they are installed in, and they require hardware in every bin plus ongoing battery and maintenance. Camera-based detection covers overflow, illegal dumping, littering, and area accumulation across a whole zone on existing CCTV, and adds road monitoring on the same feed. The honest trade-off is that sensors give exact per-bin fill numbers, while cameras give far broader coverage, offender evidence, and lower hardware cost.

Yes. The system tracks contractor pickups with timestamped evidence and uses before and after camera validation to confirm a bin area was genuinely cleared, not just visited. An SLA dashboard shows which contractor zones are compliant and which are failing, with automated reports for municipal review or private-client reporting. This turns waste-collection SLAs from a paperwork exercise into verifiable data.

Yes. Every pothole and garbage detection is tagged with GPS coordinates and mapped to the responsible municipal ward or campus zone. The dashboard shows defect and waste density per ward so administrators can see exactly where attention is needed, and automated work orders route to the right maintenance team. This ward-level accountability is one of the most valued features for municipal buyers.

Cost depends on the number of cameras, whether vehicle-mounted units are added, and the deployment scale, so it is quoted per project. Because the system runs on existing CCTV and needs no per-bin sensors, it is typically far cheaper than IoT or LiDAR alternatives, and both modules run on one platform; the Tentovision Edge Device starts at around ₹6,000 (indicative). Pricing is transparent and project-specific, and a pilot lets you validate value before scaling. See pricing.

Yes, and it is the recommended approach. A pilot of 10 to 20 cameras on a road stretch or a single campus validates detection accuracy in your specific conditions — lighting, camera angles, road and waste types — before any city-wide or estate-wide rollout. The pilot produces real detection data and geo-tagged evidence you can evaluate, and it scales seamlessly into full deployment on the same platform.