# Adaptive Feature Fusion for Small Object Detection
###### Abstract  
In order to alleviate the situation that small objects are prone to missed detection and false detection in natural scenes, this paper proposed a small object detection algorithm for adaptive feature fusion, referred to as MMF-YOLO. First, aiming at the problem that small object pixels are easy to lose, a multi-branch cross-scale feature fusion module with fusion factor was proposed, where each fusion path has an adaptive fusion factor, which can allow the network to independently adjust the importance of features according to the learned weights. Then, aiming at the problem that small objects are similar to background information and small objects overlap in complex scenes, the M-CBAM attention mechanism was proposed, which was added to the feature reinforcement extraction module to reduce feature redundancy. Finally, in light of the problem of small object size and large size span, the size of the object detection head was modified to adapt to the small object size. Experiments on the VisDrone2019 dataset showed that the mAP of the proposed algorithm could reach 42.23%, and the parameter quantity was only 29.33 MB, which is 9.13% + 0.07% higher than the benchmark network mAP, and the network model was reduced by 5.22 MB. multi-scale feature fusion; adaptive fusion factor; attention mechanism; small object detection +
Footnote †: journal:  
In object detection, it is generally considered that the size of the object is a small object when it is small relative to the size of the original image, and the existing small object definition methods are mainly divided into absolute size definition and relative size definition [11]. Taking the definition of COCO [12] objects, a common dataset in the field of object detection, COCO object definition, the object below 32 \(\times\) 32 pixels in the figure is a small object. The relative size definition defines a small object from the relative ratio of the object to the image, and objects that are less than 10% of the image size or more are small objects [13]. In this paper, according to the definition of small objects in the general dataset COCO, objects smaller than 32 \(\times\) 32 pixels are defined as small objects. Because a small object has few pixels, its feature expression ability is weak, and there is less representative feature information. In order to enhance the expression ability of small object features, [14] adopted a top-up feature pyramid FPN feature fusion strategy to fuse deep features with shallow features, which increased the possibility of object detection, but also increased the background information. [PERSON] et al. [15] proposed the Squeeze and Excite (SE) block, which dynamically refines the features and significantly enhances the feature representation ability, but only focuses on enhancing the channel information of the features. R2-CNN [16] introduced the attention model to reduce the influence of complex background on small object detection and the false alarm rate, but due to the lightweight of its backbone network, the feature extraction ability was reduced. [PERSON] et al. [17] proposed a feature enhancement module (FEM) for the problems of the low amount of effective information for feature extraction of the backbone network and the weak ability of feature map information representation. In this way, the multiple receptive field features in the lower-level feature map are fused, which enhances the feature extraction ability, but at the same time, brings a lot of feature redundancy. It can be seen from the above that the detection of small objects in natural images faces the following challenges:  
* The small object has few pixels, and the object scale spans large;
* The expression ability of small object features is weak, and the detection accuracy is low;
* The background image of the small object is complex, the background information is highly similar to the small object, and it is easily disturbed by the background.