# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 2 Related Work  
The definition of small objects can be divided into two forms: absolute size-based small objects and relative size-based small objects. Absolute size-based small objects are defined based on the absolute number of pixels in the object. For example, in the COCOdataset [9], small objects are defined as objects with a resolution smaller than 32 \(\times\) 32. Since the resolution of images in different datasets varies, the specific definition of small objects also differs. Relative size-based small objects are usually defined based on the ratio of the object's bounding box width and height to the image width and height. Objects with a ratio less than 0.1 are often defined as small objects. Solutions for small object detection can be divided into two aspects: improvements in input image preprocessing and network structure. In terms of input image preprocessing, super-resolution techniques [10] can be used to obtain detailed information about small objects. Additionally, image reconstruction algorithms based on Generative Adversarial Networks (GANs) [11] can more effectively extract features of small objects. In terms of network structure improvements, deep networks in convolutional neural networks acquire more comprehensive semantic information, which is contradictory to the small size and low resolution of small objects. Therefore, it is necessary to combine the feature information of deep networks with the positional information of shallow networks to achieve multi-scale feature fusion [12] and improve the feature extraction ability of small objects. There are also studies on context information extraction [13] and feature channel attention [14] and spatial information attention [15], as well as improvements in detection head networks for better performance in small object detection tasks. Next, we briefly introduce object detection algorithms based on deep learning. With the widespread use of convolutional neural networks and the improvement of computer hardware capabilities, object detection algorithms based on deep learning have made significant progress. According to whether there is a separate step for obtaining candidate boxes, the algorithms can be divided into one-stage object detection algorithms [16] and two-stage object detection algorithms [17]. Compared to two-stage object detection algorithms, the main advantage of one-stage object detection algorithms is that they omit the step of candidate box extraction, resulting in faster detection speed. The important milestones of object detection algorithms are shown in Figure 1.  
Two-stage object detection algorithms consist of two parts: region proposal part and feature classification and regression prediction part. The first part mainly performs binary classification of the background and foreground of the input image, selects the position of the region of interest (RoI) by boxing it with a rectangular box, and obtains the coordinates of the upper left and lower right corners of the rectangular box to determine the specific location of the region of interest. The second part extracts features from all candidate regions obtained in the previous step, inputs them into a classifier for multi-classification tasks to determine the specific category of the object, and finally completes the regression prediction to refine the candidate boxes and complete the entire process. Common two-stage object detection algorithms include R-CNN [18], Fast R-CNN [19], Faster R-CNN [20], FPN [21], Cascade RCNN [22], and Grid RCNN [23]. The process of two-stage object detection is shown in Figure 2.  
Figure 1: The milestones of traditional object detection algorithms, one-stage object detection algorithms, and two-stage object detection algorithms. The pioneering work of one-stage object detection algorithms is the YOLOv1 object detection algorithm [24], which omits the process of extracting feature candidate boxes and directly divides the input image into regions.