A leading waste and recycling operator needed to catch unsafe behavior on its conveyor lines in real time, with a hard rule: it had to run on-prem and keep working if the site lost connectivity. RTS Labs built a computer vision system that watches every station continuously and dispatches AI agents to document and route each event into Field1st, RTS Labs' own field-safety platform.
High-Throughput Recycler
Vision AI & Agentic Safety Automation
NVIDIA DeepStream
RTMPose + Triton
ByteTrack + OpenCV
Field1st
Our engineers will map your workflow and define a ship date in a 2-week Discovery Sprint.
On a material recovery sort line, safety is hard to see and harder to prove. Workers move fast alongside conveyors, balers, and heavy machinery, and the riskiest behaviors, reaching across a running belt, crowding a neighbor, stepping into an exclusion zone, happen in seconds. The client’s safety program leaned on supervisors hitting an observation quota across a set of routes, so most of the line and most of every shift went unwatched. Manual inspections took 30 to 35 minutes each and depended on memory, familiarity, and judgment, which meant calls were slow, inconsistent, and easy to miss.
Existing telematics cameras pushed some alerts but caught almost none of the near-misses on the fixed sort lines. And nobody could measure the exposure that actually drives most injuries in waste handling: repetitive over-reach and torso lean, sustained across a shift.
Leadership set one non-negotiable before evaluating any solution. The system had to detect and warn in real time, run locally on-site, and keep working even if the facility lost its internet connection. Anything cloud-dependent was off the table.
Coverage depended on supervisors meeting an observation quota across 10 to 15 routes, so most of the line and most of every shift went unwatched.
Manual safety inspections took 30 to 35 minutes and leaned on memory, familiarity, and bias, producing inconsistent calls that were easy to miss.
Strains and sprains are the most common waste-handling injury, driven by repetitive over-reach (~80cm to the belt midpoint vs a ~50cm safe limit), yet nobody could quantify it across a shift.
RTS Labs built a three-layer system that keeps the intelligence at the edge and only reaches for a large model when it matters. Neural pose models read the scene in real time on an on-site device; a deterministic geometry-and-rules engine grades what they see, so every call is explainable rather than a black box; and when a violation fires, AI agents take over to confirm, document, and route the event into Field1st. Because the vision events share Field1st’s existing safety taxonomy, each detection lands as a record the team already knows how to action and feeds the next day’s job briefing.
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