# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
###### Abstract  
In the process of image shooting, due to the influence of angle, distance, complex scenes, illumination intensity, and other factors, small targets and occluded targets will inevitably appear in the image. These targets have few effective pixels, few features, and no obvious features, which makes it difficult to extract their effective features and easily leads to false detection, missed detection, and repeated detection, thus affecting the performance of target detection models. To solve this problem, an improved faster region convolutional neural network (RCNN) algorithm integrating the convolutional block attention module (CBAM) and feature pyramid network (FPN) (CF-RCNN) is proposed to improve the detection and recognition accuracy of small-sized, occluded, or truncated objects in complex scenes. Firstly, it incorporates the CBAM attention mechanism in the feature extraction network in combination with the information filtered by spatial and channel attention modules, focusing on local efficient information of the feature image, which improves the detection ability in the face of obscured or truncated objects. Secondly, it introduces the FPN feature pyramid structure, and links high-level and bottom-level feature data to obtain high-resolution and strong semantic data to enhance the detection effect for small-sized objects. Finally, it optimizes non-maximum suppression (NMS) to compensate for the shortcomings of conventional NMS that mistakenly eliminates overlapping detection frames. The experimental results show that the mean average precision (MAP) of target detection of the improved algorithm on PASCAL VOC2012 public datasets is improved to 76.2%, which is 13.9 percentage points higher than those of the commonly used Faster RCNN and other algorithms. It is better than the commonly used small-sample target detection algorithm.  
CF-RCNN; CBAM; FPN; target detection; attention mechanism +
Footnote †: journal:  
annotation method, it shows better performance in practical applications. Therefore, applying the convolutional neural network to assist target detection can enhance the ability of the model to extract image features and improve the accuracy of model object classification.  
For the VGG16 network model [2] in object recognition applications, due to the shallow layer, the summary of object features is insufficient and the expression ability is weak. In order to solve such problems, a CF-RCNN algorithm integrating the CBAM (Convolutional Block Attention Module) attention mechanism [3] and FPN (Feature Pyramid Network) feature pyramid structure [4] is proposed. The algorithm uses the VS-ResNet network with a stronger expression ability and deeper layers to replace the VGG16 network in the feature extraction module of the traditional Faster RCNN algorithm, which strengthens the ability of the target detection model to analyze image features; VS-ResNet changes the ResNet-50 network [5] to the group convolution [6] mode, reducing the number of super parameters and the complexity of the algorithm model. The original residual structure is changed to an inverse residual structure [7], so that the ReLU activation function [8] can better save function information. The risk priority number (RPN) is optimized to reduce the error rate of candidate frame filtering in the case of dense objects. Based on the traditional Faster RCNN algorithm, CF-RCNN fuses the FPN feature pyramid structure to improve the detection capability for small targets; the CBAM attention mechanism is introduced to focus on efficient information, to improve the accuracy of truncated or occluded object detection.