Faster R-CNN, for example, has a region proposal network, which you might be able to use. However, as far as I know, most region of interest (ROI) or region proposal networks (RPN) are still biased towards detecting objects which are close to the task (and hence classification) they were trained on. You could also use a standard object detection model, like Yolo, and reduce the threshold of the classification to be low enough to propose object location without needing a high confidence. You can then simply discard the proposed classification and only keep the bounding box localization.