Object detection and object recognition are both tasks in computer vision, but they have distinct differences.

  Object detection aims to not only identify the presence of objects in an image, but also localize their locations by drawing bounding boxes around them. It involves finding multiple objects of interest within an image and classifying each object into specific categories. The key component in object detection is the ability to detect and classify multiple objects simultaneously.

  On the other hand, object recognition is concerned with identifying specific objects within an image or a video frame. It involves assigning a label or a category to the entire image or a specific region of interest, without precisely localizing the object. Object recognition focuses on identifying the content of an image or a scene, and it does not involve drawing bounding boxes.

  To summarize, the main difference between object detection and object recognition lies in the level of detail and localization. Object detection performs both localization and classification, while object recognition only involves assigning labels or categories to the entire image or a region of interest.

  It is worth mentioning that object detection is typically more challenging than object recognition due to the need for localization and handling multiple objects in an image. Object detection is widely used in various applications, such as self-driving cars, surveillance systems, and robotics, where precise localization and identification of multiple objects are crucial.