# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 1 Introduction  
In recent years, computer technology has entered a period of rapid development. Images as an important carrier of information transmission have also reached new heights. Computer vision, as an important branch of computer technology [1], has become a popular field of research for image processing and analysis. Object detection technology [2] is a key research direction in computer vision. Traditional object detection methods such as HOG features [3] and SURF features [4] suffer from poor generalization capabilities and require complex feature extraction optimization algorithms for different scenes and tasks. With the advancement of hardware devices and the efficient combination of computing power and convolutional neural networks, deep learning-based object detection algorithms are becoming more practical and efficient. Deep learning has pointed to new directions in the development of object detection methods. Conventional-sized object detection tasks have achieved good detection results. However, small object detection tasks pose a significant challenge due to the limited feature information that can be extracted and susceptibility to background interference [5]. Small objects are widely present in practical scenarios such as remote sensing images [6], unmanned aerial vehicle (UAV) images [7], and autonomous driving road condition images [8]. As small objects often have a wide distribution range and can overlap with one another, it becomes difficult to extract their features, increasing the difficulty of feature extraction and detection. Therefore, improving small object detection models andenhancing their recognition accuracy and effectiveness has become particularly important in extending the theoretical research of small object detection to practical application scenarios such as aerial image detection, which will contribute to better application value for human society. The main reasons for the challenges faced by the field of small object detection based on deep learning are as follows:  
1. Excessively increasing the depth of the feature extraction network for small objects with small size and resolution leads to the loss of their own feature information. Increasing the receptive field size during pooling leads to problems such as image resolution reduction and loss of image details. 2. Small objects are easily confused with the background in complex background environments, making it difficult to extract their feature information through feature extraction. 3. Multiple downsampling operations in the detection head network of the object detection algorithm cause a loss of small object details. 4. The coupled network in the object detection algorithm processes classification and bounding box regression tasks simultaneously, which weakens the performance of both tasks. 5. The bounding box regression loss function in object detection algorithms has different spatial sensitivity to objects of different scales, resulting in low regression prediction accuracy for small objects and easy missed detection of adjacent objects. The contributions of this paper are as follows:  
1. This paper proposes an improved small object detection method based on the official version of the originla YOLOv5s algorithm, namely DMS-YOLOv5. 2. The receptive field module is introduced into the feature extraction network to increase the network's receptive field, allowing the network to focus more on small objects with small size and low resolution and complete the extraction of multi-level semantic information. 3. The CA attention mechanism is introduced, which combines spatial and channel attention information, to reduce the interference of background information on object features and enhance the network's ability to focus on object information. 4. A small object detection layer is added to compensate for the problem of losing small object details during multiple downsampling operations. 5.