## 1 Introduction  
Pedestrian detection, which is a very important problem in computer vision, is very critical in many practical fields, such as the automatic driving and security fields. Because of its high practicality, it has higher requirements for accuracy and speed than general object detection. Traditionally, in the field of object detection, a method based on sliding windows is usually used, and it is generally necessary to design and select features manually, so the effect is usually not ideal. Then through the use of R-CNN [1], the convolutional neural network (CNN) was introduced into object detection. This two-stage object detector based on deep learning opened a new era in which deep learning occupies the mainstream position in object detection. The subsequent Faster R-CNN [2] was used to propose the region proposal network(RPN) to generate proposals, which greatly improved the speed and effect. Because of its great success in object detection, it has become the most important and popular framework in pedestrian detection. The subsequent one-stage methods are faster than the two-stage methods represented by Faster R-CNN because they do not need to generate proposals before classification and regression, but their accuracy is lower. Among them, SSD [3] is one of the early representatives, and it directly classifies and regresses anchors to get bounding boxes without having to generate region proposals first. However, the detection effects of existing methods based on deep learning are directly related to the features that can be utilized, which range from lower-level edge features and corner features to higher-level semantic features. Feature utilization, such as through an RPN, only predicts at the last layer of feature maps, which indicates that it only utilizes the feature map of higher-level semantic features with smaller size, but makes inadequate use of the feature maps of lower levels but larger size. Therefore, it is difficult to give a good solution to the problems of how much feature maps should be utilized and how to utilize the feature maps adequately. The second method is used to detect objects of different scales on feature maps of different scales, as proposed in SSD, which leads to the problem that adjacent objects may be detected on different feature maps only due to small-scale differences, thus splitting the semantic information of each feature map and leading to new problems. The third method, FPN [4], performs feature fusion through the feature pyramid, but the problem of the fragmentation of the semantic information is still exists like in SSD, and it is increasingly difficult to achieve good results for complex tasks due to the relatively simple feature fusion method. Therefore, making rational use of the feature maps of various scales has become the key to all of these. Above all, there are two main problems to be solved that exist in current detectors: (1) the problem of inadequate multi-scale feature fusion, which makes it difficult to fuse features of different scales in adequate proportions; (2) the problem that, when there are many feature fusion modules like Multi-Scale Feature Fusion Units (MFFUs), it is difficult to adequately fuse the features already fused by the MFFUs at multiple levels. It may be because only the higher-level features are retained, or some lower-level features are retained but the retention ratio is difficult to determine. In other words, there will be a loss of lower-level feature information in multi-level feature fusion modules. To solve these challenges, our idea is to adopt adaptive feature fusion as the core method. We propose our original MFFU as an adaptive feature fusion module, which makes it possible for the model to fuse the feature maps with different scales in adequate proportions.