xby-ebike-detection

In smart-transportation, urban-security, and insurance-claim workflows, pinpointing electric bicycles within a single image is a deceptively basic yet frequently overlooked task. General-purpose object detectors either lump e-bikes into a broad "vehicle" class or simply miss two-wheeled electric bikes altogether. The xby-ebike-detection plugin boils this down to a single inference call: hand it an image, and it returns the bounding-box coordinates, confidence scores, and labels for every e-bike in the frame — no model deployment or post-processing pipeline required on your side.

Three input modes are provided to match different integration styles: a remote image URL, a BASE64-encoded string, or a local file path. This lets the same detection logic slot into server-side APIs, front-end uploads, or offline batch jobs without code changes. The API key is written once via a chat command and persists automatically across session restarts, eliminating repeated authentication steps.

It is a good fit for developers building traffic-monitoring, shared-mobility management, or insurance-assessment systems, as well as automation engineers who want a plug-and-play "image in, structured detections out" step inside a DeepSeek Harness workflow. If you need e-bike detection results without training your own model, this plugin is worth a look.