Does object detection for single-class images have any advantages over classification?

Question

I recently joined a new project, and saw that they are using object detection instead of image classification for one of the business cases. The images can only belong to one class (example, the image is either a cat or something else) and the location of the object does not matter. They just want to detect the presence of it. The training/test data have bounding boxes drawn over the objects, and I am trying to find evidence, if any, as to why this method has any advantages over normal classification. My initial though was that with bounding boxes, provided they are accurately drawn, the model can learn key features better than usual classification, since in classification it is learning the entire image, but I am not sure, because in that case, then most classification problems would have bounding boxes drawn over the key object to improve results.

Are there any advantages of the current approach or is classification the 'right' option?

Answer 1

I think you are right, having bboxes help the model localize objects much better, and usually, the classification comes with each bbox it found so the space now will be reduced to fit just one important object that it is trying to make a prediction, not the entire image where there are a lot of objects.

Classification is only good if your input has information controlled, or else it can cause confusion for the model as the input may not have a single label for the whole image.