# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 1 Introduction
Feature maps fused with multi-scale features have better robustness when detecting objects of different sizes; however, in the bottom-up backbone network, the path from the low-level features to the top-level features is long (such as Resnet50 and ResNet101 [19]), and some high-resolution information is lost in the process of upsampling, which increases the difficulty of obtaining accurate positioning information. In view of the above two reasons for the low utilization of low-level high-resolution information, this paper makes the following improvements to the standard RetinaNet:  
First, to supplement and improve the utilization of high-resolution features, inspired by PANet [20], this paper introduces a bidirectional feature pyramid structure, which fully combines deep and shallow information for feature extraction, thereby improving the expression of target semantic information by the shallow feature network. Based on the standard RetinaNet, this paper adds a bottom-up enhancement path, which greatly shortens the propagation path of low-level information and improves the utilization of low-level information. The semantic and contextual information of small and medium-scale objects is enhanced by bottom-up branching. In addition, the bidirectional feature fusion also fully integrates multi-scale information, which is beneficial to the target detection effect. Second, to make full use of the high-resolution information, this paper reuses the low-level feature C2 of the backbone network. At the same time, in order to solve the shortcomings of the limited receptive field of low-level features and weak semantic information, and inspired by DetNet [21], this paper uses dilated convolution to expand the receptive field and connect it laterally with P3 from the top-down path. In addition, to enhance the feature fusion between the features extracted from different receptive fields, inspired by AC-FPN [22] and DenseNet [23], this paper connects dilated convolutions with different dilation rates in a residual connection. Third, to reduce the loss of high-level semantic features caused by the bidirectional feature pyramid structure, this paper designs high-level features C6 directly from the backbone network and supplemented by low-level high-resolution features. It does not need to go through a top-down fusion path and fuses information from lower layers to obtain rich localization information while minimizing the loss of semantic information. Combining the above analysis and strategies, the detection method proposed in this paper has the following advantages:  
(1) To shorten the propagation path of high-resolution information and improve the utilization of high-resolution features, this paper introduces a bidirectional feature pyramid structure. (2) To take full advantage of the low-level features, this paper designs the Dilated Feature Enhancement structure to extract the high-resolution information of the low-level features through the dilated convolution of residual connections. (3) To compensate for the high-resolution information lost during the long propagation process of the backbone network, this paper designs the High-level Guided Model, which is guided by high-level features to extract high-resolution information in lower layers.