Vehicle detection: Datasets & Models
Vehicle detection locates and classifies cars, trucks, and other road vehicles in images or video frames. It's a foundational computer vision task for traffic monitoring, parking management, and automotive analytics.
Vehicle detection
This vehicle detection workflow runs object detection on images or video frames to locate and classify road vehicles such as cars, trucks, buses, and motorcycles, returning a class label and bounding box coordinates for each instance. It accepts still images or video streams as input and outputs structured detections suitable for traffic monitoring, parking management, and automotive analytics pipelines.
Try it out
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Why automate vehicle detection
Manually reviewing camera footage to count vehicles, monitor lot occupancy, or analyze traffic flow is slow, inconsistent, and impossible to scale across many feeds. Automating vehicle detection with computer vision gives you continuous, frame-by-frame analysis that never blinks, so automotive and traffic teams can measure throughput, detect congestion, or trigger events in real time. It removes the labor bottleneck of human observers and produces consistent, queryable data instead of subjective spot checks. For automotive applications like ADAS prototyping, fleet analytics, or smart-parking systems, reliable detection is the prerequisite for every downstream decision.
Key benefits
A trained vehicle detection model runs against your existing traffic and lot cameras without new hardware, turning raw video into structured counts and locations. It scales horizontally across dozens or hundreds of feeds and integrates cleanly with tracking and analytics pipelines. Using RF-DETR as the recommended model gives you strong accuracy on crowded, overlapping scenes while staying fast enough for real-time deployment.
How to use it
- Upload an image: Drop any photo into the widget above to try vehicle detection for free, right on this page.
- Preview instant results: The model detects car, truck, bus, motorcycle, van 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 vehicle 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 vehicle detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Vehicle Detection project to Roboflow Agent.