## 2 Methods
### Enhanced Feature Module  
Feature enhancement is essential for object detection. In recent years, deep CNN has been extensively used in image recognition. A deep convolutional network can learn semantic information of an image, which can better figure out the objects in the image. With the outstanding breakthrough of deep learning algorithms, many scholars explore feature enhancement methods to improve the feature representation ability of CNN. One of the efficient feature enhancement methods is the multi-scale feature fusion. High-level features generated by deep CNN have stronger semantic information. Shallow features contain more edge and contour information, which is conducive to localization. Usually, the last feature layer of CNN is well used for detection, but the information of the top and bottom layers cannot be fully used for detection. Therefore, how to better integrate high-level semantic features and low-level spatial information is the focus of feature enhancement methods. Feature pyramid network (FPN) [9] is a classical network for multi-scale feature enhancement. The goal of FPN is to create a feature pyramid structure that integrates low-level and high-level layers through a top-down path. In contrast, the bottom-up path transfers low-level details from shallow layers to deep layers. PANet [29] enhances the entire feature expression with accurate localization information in lower layers through bottom-up path augmentation, shortening the information path between low-level layers and top-level layers. In addition, [PERSON] et al. [14] proposed a cross-layer network to further aggregate features in top and down layers. The attention mechanism [15], another novel feature enhancement tool in deep learning, has been widely used in object detection [30; 31; 32]. Its principle is to generate a new layer of weight and identify the key features in the image data. The classical attention methods are SENet [30],CBAM [33],SKNet [34], and GCNet [31]. However, most of these attention modules are applied on each layer of the backbone, which generates a large computation requirement. In our method, we introduced channel attention methods into our proposed enhance feature module for reweighting the output of multilevel local features. Generally, the object to be detected is only a part of the whole image, while most of the other parts are the background area. Thus, extracting effective information while ignoring background areas is crucial for object detection. In addition, high-level feature maps only contain single scale context information which does not benefit from different receptive fields. To effectively tackle these two problems, we used an enhanced feature module (EFM) for improving the feature discrimination between objects and the background. EFM contains two components, a multi-scale local attention (MSLA) module and a channel attention-guided unit (CAU). The MSLA module is used on the top-level feature map C5, which can not only avoid much computation but also capture rich context information from different receptive fields by using multi-scale dilated convolution layers. Then, a channel attention-guided unit (CAU) is used for reweighting the channel features and exploring the interdependence and correlation of multi-scale features. As shown in Figure 3, EFM includes two parts: the MSLA module used for capturing multi-scale local features from the high-level feature map, the CAU used for reducing the misleading of aliasing features caused by multiscale information and mining the interdependence among multi-level features. Given the input deep features \(C_{5}\in\mathbb{R}^{C\times H\times W}\), the output of MSLA is \(F\in\mathbb{R}^{C^{\prime}\times H\times W}\), and the output of EFM is \(X\in\mathbb{R}^{C^{\prime}\times H\times W}\).