# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 1 Introduction  
Object detection is one of the core problems in the field of computer vision research. With the development of the convolutional neural network [1], many advanced detectors based on deep learning have appeared in recent years. At present, there are two types of mainstream object detection algorithms. One is the two-stage object detection based on the candidate box represented by R-CNN [2] and its variants, such as Fast R-CNN [3], Faster R-CNN [4], Mask R-CNN [5], etc. The two-stage object detection is developing rapidly, and the detection accuracy is constantly improving, but the problem of its architecture leads to high network computing costs and slow detection speed, which cannot meet the real-time requirements. The other type of mainstream object detection algorithm is the one-stage object detection based on regression analysis represented by SSD [6], YOLOv1 [7] and its variants [8; 9; 10], RetinaNet [11], EfficientDet [12], etc. The one-stage object detection algorithm directly predicts the target coordinates and categories after extracting the input image features through CNN, which greatly improves the detection efficiency, but the most important deficiency is that the detection accuracy needs to be further improved. How to improve the accuracy of one-stage object detection has attracted the attention of many researchers. DSSD [13] adds a deconvolution model to SSD [6]. FSSD [14] reconstructs the pyramid feature map and integrates the features of different scales. YOLOv3 [9] solves the multi-scale problem through the residual network. RefineDet [15] combines the idea of a two-stage and one-stage target detector, adopts the regression idea from coarse to fine, and introduces the feature fusion operation similar to FPN [16]. Using the idea of a thermodynamic diagram for reference, CornerNet [17] predicts the target by predicting a group of corners. EfficientDet [12] uses a weighted bidirectional feature pyramid network for feature fusion and scales the model through a composite feature pyramid network. All ofthe above improve the object detection effect in varying degrees, but it also inevitably leads to problems such as high model complexity, difficult convergence, and slow detection speed. One of the one-stage detectors, RetinaNet [11], introduces focal loss to solve the problem of sample imbalance and achieves comparable accuracy to the two-stage object detection framework while ensuring real-time performance; however, small targets have always been a difficult point in target detection. Due to the small coverage area of small targets, low resolution, and lack of diversity in locations, various target detection methods cannot achieve the same accuracy as large targets for the detection of small targets. In particular, RetinaNet is ideal for detecting large-sized objects, but it is not effective for detecting small objects smaller than 32 \(\times\) 32 pixels [18]. By analyzing the network structure of RetinaNet, this paper found that the main reason for the low detection accuracy of small objects is the limited utilization of high-resolution information. It is mainly manifested in two aspects. First, the standard RetinaNet does not make full use of the lower feature layer C2 containing high-resolution features. The shallow layer C2 of the backbone network contains rich location information that is conducive to small target recognition. In the bottom-up path, in order to expand the receptive field of the backbone network, the feature map in the network will continue to shrink, and the value of the stride is likely to be larger than the size of the small target, resulting in the loss of the feature information of the small target during the convolution process. If the shallow information cannot be fully utilized, the small object features will be lost. Second, the standard RetinaNet uses the FPN structure, upsampling the high-level feature map, and then adding and merging with the shallow-level features to obtain a new feature map with stronger expressive ability.