Target detection algorithm based on multilayer attention mechanism-adaptive feature fusion network

Target images are complex and diverse under the influence of scale, occlusion and appearance factors in real scenes, and they affect the performance of target detection algorithms. They also make the existing target detection algorithms suffer from the following problems. On the one hand, the neurons in the target detection algorithm architecture cannot learn the complex interaction and semantic features inside the target image. On the other hand, the feature expression of different target images is insufficient and the channel reduction leads to the loss of position information and other problems. herefore, a multi-layer attention mechanism of considering both node and semantic level attention in the model architecture is proposed. In this method, the fusion of neighbors and semantic information is weighted, and node representations is learned under a hierarchical aggregation manner.Just because of this, it can improve the effectiveness and interpretability of the model, and solve the problem of complex interaction and rich semantic feature acquisition within images. Furthermore, we propose an adaptive feature fusion network which can adaptively filter the useless information of other layers and retain the feature information that is beneficial to target recognition. A feature enhancement module, is introduced to enhance the identifiability of the top-level target features of the feature network, and which can alleviate the problem of loss of target position. Finally, the extensive tests using PASCAL VOC and MSCOCO datasets, the experimental result shows that our method not only has the best recognition performance, but also has better stability and robustness.