Global-Local Attention Mechanism Based Small Object Detection

A small object detection method based on the combination of global and local attention mechanism is proposed in this paper to detect small objects distributed in images. object detection model based on local attention mechanism has good detection accuracy and speed. However, its performance will be reduced due to the smaller size of the object, especially in the case of missed detection and false detection, and the proposed Global Local Detection Model (GLD) can solve this problem. Specifically, a model solution of the Global and Local Combined Attention Block (GL-CAB) combining deep global features and shallow local features of the network is proposed to solve the problem of small object missed detection. On the one hand, the model focuses on small object s in the local and global ranges, and on the other hand, it supplements the small object information lost during the down-sampling process. Aiming at the situation of pseudo-information generated by small object feature fusion, a multi-branch feature pyramid network (MB-FPN) is proposed. Multi-input is used to form multi-scale feature maps for multi-feature fusion on different branches, which reduces the formation of pseudo-information and enhances the extraction of detailed features of small object by the network. Then, the AU-AIR and VOC2007 datasets are selected for experimental training, and the object detection evaluation indicators (AP, AR, F1, mAP, and FPS) are introduced for comparative analysis. Finally, the simulation results show that the proposed method has better performance to solve the problem of missed detection and false detection of small object.