The Detect endpoint returns an array of objects, each indicating whether a helmet is detected in the image. Each object includes a label ("Helmet") and coordinates representing the position of the detected helmet.

The key fields in the response data are "label," which indicates the detection result (e.g., "Helmet"), and "coordinate," which provides the bounding box coordinates of the detected helmet in the format [x1, y1, x2, y2].

The response data is organized as a JSON array, where each element corresponds to a detected helmet. Each element contains a label and coordinates, allowing users to easily parse and utilize the information.

The Detect endpoint provides information on whether helmets are present in the image, including the number of helmets detected and their respective positions, which can be used for safety compliance monitoring.

Users can customize their requests by providing different image URLs to analyze various images. The API processes each image independently, allowing for flexible monitoring of helmet usage across different scenarios.

Data accuracy is maintained through advanced image processing algorithms that analyze visual features to detect helmets. Continuous updates and improvements to the model help enhance detection reliability.

Typical use cases include monitoring helmet compliance in construction sites, warehouses, and logistics operations, as well as ensuring safety for bikers and cyclists. This helps prevent accidents and promotes workplace safety.

If the response contains no detected helmets, users should interpret this as either no helmets present or potential issues with image quality. It's advisable to check the image clarity and ensure it meets the API's requirements for optimal detection.

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