> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-prism/test-inventory/object-detection.md). # Object Detection ## [Computer Vision](broken://pages/yKc74cimMK1I3TYBdn25) RagaAI supports a wide range of Computer Vision applications, including Semantic Segmentation, Object Detection, Image Classification, Super Resolution, Data Curation and OCR (Optical Character Recognition). #### Object Detection The objective of object detection, a computer vision problem, is to locate and identify individual items inside an image by enclosing them in bounding boxes. This method offers a thorough comprehension of a visual scene and is used in a number of industries, including retail for consumer analytics and inventory management, medical imaging for anomaly identification, surveillance for security, and autonomous cars for obstacle recognition. RagaAI ensures that these models can accurately differentiate and label the components of an image, detect drift and performs well in diverse scenarios. *** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.raga.ai/ragaai-prism/test-inventory/object-detection.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.