## 1 Introduction  
### Problem Statement  
Object detection plays an essential role in computer vision, which has attracted a strong emphasis among researchers, resulting in the continuous development of new and more accurate object detectors. A typical object detector takes an image and produces a set of rectangles, so-called bounding boxes (BB), which define the borders of objects in the input image. The detector quality is measured as an overlap between the detected boxes and ground truth (GT) boxes using intersect over union (IoU). The more descriptive statistic, mean average precision (mAP), uses a set of IoUs with various thresholds, where a threshold distinguishes between the acceptance/rejection of detected boxes. Existing solutions for object detection yield accuracies around 30-55 mAP on the COCO dataset [1]. Such a score allows usage in many real applications, but, on the other hand, there is space for improvement. A combination of the following may achieve such growth: more precise classification, an increase in the rate of true-positive detection, a decrease in false-positive detection, or an improvement in the IoU. There are four reasons why object detection may be difficult. A neural network has to find all objects in an image; the number of objects may vary from zero to hundreds. A neural network has to be sensitive to all possible sizes of an object; the same object class may be tiny or occupy the whole image. A network usually has no a priori information, which should make the detection easier, such as the context of the scene or the number of objects. There is a lack of satisfactory large datasets; therefore, the data distribution is sampled roughly. In this study, we propose BBRefinement, a specialized, one-purpose object detector. It is trained on objects that are cropped from the input images together with augmented information about the object's class and the dimensions of the bounding box. The purpose is to refine an imprecise bounding box dimension into a precise form. During the training stage, it is trained fully standalone. During the inference stage, it is coupled with an arbitrary object detector and its prediction is refined. The proposed scheme is able to suppress the effect of all four mentioned difficulties, resulting in a higher mAP.  
**Our contributions to the problem are as follows:**  
* We propose BBRefinement, a specialized, one-purpose object detector that processes mixture data and can work as an extension to any object detector to improve its accuracy.
* We designed a specialized augmentation technique that combines the augmentation of information about the object's class and bounding box dimensions.
* We define two losses, the first aiming at metrics that involve a single IoU threshold, and the second minimizing metrics that involve multiple IoU thresholds.  
**The advantages of our proposed method are the following:**  
* BBRefinement is universal. It can be trained only once and then coupled with an arbitrary object detector that produces the same object classes.
* The proposed scheme is capable of suppressing known difficulties in the object detection area, thus resulting in a higher mAP, mainly for large and medium-sized objects.
* It is fast, and adds only 50 ms overhead per predicted image.
* It can be used to improve the quality of imprecise labels.