By Luis Lopez, AI transportation consultant, CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast
Most warehouses and yards already have cameras. They record, someone reviews footage after an incident, and the rest of the time they produce hours of video nobody watches. Computer vision changes that by turning the video stream into data: this trailer arrived, that door is empty, a pallet is sitting where it should not be. The idea is simple. The reality depends heavily on lighting, camera placement, and how well the system was tuned to your specific site.
As an AI transportation consultant, the question I think matters most is what these systems can actually see. Here is my attempt at a grounded answer.
What computer vision does well
Computer vision is strongest when the task is narrow, visual and repetitive. Some of the more dependable uses in logistics facilities include:
- Reading identifiers. Trailer numbers, container numbers, license plates and placards are printed text in predictable places. Optical character recognition at gates and dock doors can capture them without a clerk with a clipboard.
- Counting and presence. Is a trailer in this door? Is a parking slot occupied? How many pallets are staged in this lane? Presence detection is far more reliable than judging condition.
- Zone monitoring. Detecting people or vehicles in restricted areas, or forklifts entering pedestrian walkways, is a well-established safety use.
- Event timing. Timestamping when a trailer arrives, is placed at a door, and departs gives you dwell data that is otherwise reconstructed from memory.
Notice the pattern. These are all questions with a fairly clear visual answer. The harder the question is to answer from a still photograph by a trained person, the harder it will be for software.
What it does less well
This is where expectations often run ahead of capability.
Judging damage and condition
Spotting a crushed corner on a carton under good light is plausible. Deciding whether a pallet’s wrap is adequate, whether freight shifted in transit, or whether a minor dent matters for a claim is much harder. Damage detection tends to need controlled imaging conditions, such as a fixed station with consistent lighting, rather than a camera pointed down a busy aisle.
Anything hidden
Cameras see surfaces. They cannot tell you what is inside a closed trailer or whether a sealed carton contains what the label says. Teams sometimes expect a camera to substitute for a physical inspection. It does not.
Bad conditions
Glare off wet pavement, headlights at night, rain, fog, dirt on lenses and low sun angles all degrade accuracy. A system that works well at noon in a demo can behave very differently at 5 a.m. in winter. Ask to see performance across shifts and weather, not a highlight reel.
Judgment calls
Whether a driver is behaving unsafely, whether an unfamiliar vehicle belongs, or whether a delay is justified requires context that video alone does not supply. Systems can flag, but a person has to decide.
Warehouse use cases
Inside the building, the most common applications are inventory and safety. Cameras on ceilings or on forklifts can help with pallet location tracking, dock door status, and verifying that a staged load matches the paperwork at least in count. Some operations use imaging at a fixed station to capture dimensions, labels and condition on each inbound pallet, which creates a visual record that helps if a damage dispute comes up later.
The value there is evidence and consistency, not replacing the receiving clerk. If you are thinking about the broader picture of AI in warehouse operations, Top 10 Uses of AI in Warehousing and 3PL Operations covers other areas where it tends to show up.
Truck yard use cases
Yards are a natural fit for vision because the objects are large, the identifiers are painted on the outside, and the questions are basic: who is here, where are they parked and how long have they been sitting. Gate cameras can log arrivals and departures, read trailer and container numbers, and capture the condition of equipment at entry and exit. That interchange record is useful whenever there is a disagreement about who was responsible for a scratch or a missing component.
Inside the yard, overhead or pole-mounted cameras can maintain a live picture of which spots are occupied, which helps yard staff find equipment without driving the lot. Paired with scheduling data, this supports better door assignments and shorter waits. For the scheduling side of that equation, see AI Dock Scheduling and Yard Management.
Detention disputes are another area where timestamped visual evidence matters. When a carrier says they waited and a facility says they did not, a record of arrival and departure times settles a lot of arguments. For the operational side of that problem, see how to reduce truck detention time.
What decides whether it works
- Camera placement. Angle, height and distance matter more than resolution. A cheap camera in the right spot beats an expensive one in the wrong spot.
- Lighting. Consistent light is the quiet factor behind most good results. Yards often need added lighting or cameras designed for low light.
- Site-specific tuning. Models trained on general images need to be validated on your trucks, your markings and your layout.
- Integration. A detection that stays inside a dashboard is a curiosity. It becomes useful when it updates a yard system, a work order or a shipment record automatically.
- Exception handling. What happens when a plate cannot be read or a trailer number is obscured? There should be a clear path for a human to fix it.
Privacy and workforce considerations
Cameras that identify vehicles are one thing. Cameras that monitor people are another. Before deploying, decide what you are monitoring and why, tell employees and visitors plainly, and limit who can access footage and for how long. Rules on workplace monitoring and recording differ by jurisdiction, so involve counsel rather than assuming a vendor has handled it. Systems used for safety coaching tend to be accepted better when they are framed around hazards and not around surveillance.
How to evaluate a pilot honestly
Pick one measurable problem, such as manual gate logging time, trailer search time or dock door idle time, and record the baseline before you install anything. Run the pilot long enough to cover different shifts and weather. Track three numbers: how often the system is correct, how often a person has to intervene, and what changed in the operation as a result.
Be wary of accuracy figures quoted without context. A system that reads plates correctly most of the time still creates daily exceptions at a busy gate. Ask what the failure cases look like and how quickly staff can resolve them.
The practical takeaway
Computer vision is a good tool for identifying, counting and timestamping things that are visible. It is a weaker tool for judging condition, intent and anything hidden. Treat it as a way to generate reliable records and flag exceptions, then keep skilled people responsible for the decisions that follow. Sites that start with one narrow, measurable use usually learn what their cameras can really do, and what they cannot, before spending on anything larger.
For more straight talk on AI in freight, subscribe to the Freight Guru Podcast.
About the author: Luis Lopez is a Miami-based AI transportation consultant and logistics entrepreneur, the CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast.
