Box detection: Datasets & Models
Detect and localize cardboard boxes in images and video from warehouse and logistics environments. Useful for counting, tracking, and monitoring boxes on conveyors, pallets, and loading docks.
Box detection
This detection workflow processes images and video from warehouse and logistics environments to perform cardboard box detection, drawing a bounding box around each carton it finds. It accepts still images or live camera feeds as input and outputs box locations for downstream tasks like counting, tracking, and monitoring boxes on conveyors, pallets, and loading docks.
Try it out
Powered by general detection model
Run on custom image
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Why automate box detection
Manually counting boxes, verifying pallet loads, or auditing inbound and outbound shipments is slow, error-prone, and doesn't scale during peak volume. Automating cardboard box detection lets teams reconcile inventory counts, flag misplaced or missing cartons, and trigger alerts without pulling staff off the floor. Because detection runs continuously, you catch discrepancies at the point they happen rather than during a downstream audit. This reduces shipping errors and gives operations managers accurate, timestamped visibility into box flow.
Key benefits
Detection models like RF-DETR can run on your existing warehouse cameras, avoiding new hardware investment while delivering consistent, around-the-clock accuracy. The workflow scales from a single conveyor line to an entire facility and integrates with WMS, dashboards, and alerting systems via API. You get measurable improvements in count accuracy, throughput visibility, and load verification without adding manual labor.
How to use it
- Upload an image: Drop any photo into the widget above to try box detection for free, right on this page.
- Preview instant results: The model detects cardboard box, pallet, conveyor belt, shipping label, forklift within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for box detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for box detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Box Detection project to Roboflow Agent.