# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 2 Related Works  
In recent years, many scholars have performed active exploration in the field of small-scale target detection and truncated target detection and have achieved good results. [PERSON] et al. [9] presented a modified GrabCut algorithm for localizing vehicle plate numbers. The approach extends the use of the traditional GrabCut algorithm with the addition of a feature extraction method that uses geometric information to give accurate foreground extraction. [PERSON] et al. [10] proposed a multi-scale feature attention fusion network named parallel feature fusion with CBAM (PFF-CB) for occlusion pedestrian detection. Feature information of different scales can be integrated effectively into the PFF-CB module. The PFF-CB module uses a convolutional block attention module (CBAM) to enhance the important feature information in space and channels. The authors in [11] proposed the DF-SSD algorithm, which is based on a dense convolutional network (DenseNet) for detection and uses feature fusion. It uses the algorithm framework of the Single Shot MultiBox Detector (SSD) [12] for reference and introduces DenseNet-S-32-1 to replace the original VGG16 network. In addition, DF-SSD also uses a multi-scale feature fusion mechanism. Their proposed residual prediction module is designed to enhance feature propagation along the feature extraction network, i.e., to use a small convolution filter to predict the target class and the offset of the bounding box position. The authors in [13] proposed an improved double-head RCNN small target detection algorithm. A transformer and a deformable convolution module are introduced into ResNet-50, and a feature pyramid network structure CARAFE-FPN based on content perception feature recombination is proposed. In that regional recommendation network, the anchor generation scale is reset according to the distribution characteristics of the small target scale, so that the detection performance of the small target is further improved. In [14], the authors proposed a BiFPN structure in the EfficientDet network, which is composed of a weighted bidirectional feature pyramid network, and added cross-scale connection to enhance the representation ability of features, and added corresponding weight to each input to better perform small target detection task. In [15], the authors performed deep optimization based on the YOLOv5 architecture and proposed a multi-scale multi-attention target detection algorithm YOLO-StrVB is based on the STR (Swin Transformer) network structure. The method mainly aims at small target detection under a complex background, reconstructs the framework to build a multi-scalenetwork, increases the target detection layer, and improves the target feature extraction capability under different scales. Then, a bidirectional feature pyramid network is added for multi-scale feature fusion, and a jump connection is introduced. On this basis, the baseline backbone network end integrates STR architecture. [PERSON] et al. [16] proposed a deconvolutional single shot detector (DSSD) algorithm, in which ResNet with stronger learning ability is used as the backbone network, and a deconvolution layer is introduced to further reduce the missing rate of small targets. [PERSON] et al. [17] started from the perspective of training, considered the level of data, and selectively back-propagated the gradient of target instances with different sizes according to different changes in image scale, to realize scale normalization on an image pyramid (SNIP). While capturing the change in the object as much as possible, the model was efficiently trained with objects with appropriate proportions, and the detection performance was significantly improved. Although the above algorithms improve the detection performance of the model to a certain extent, they do not fully consider the effect of local features on truncated target detection, and fail to optimize the light weight of the feature extraction module.