Military Target Detection With Hierarchical Feature Enhancement Technology

In recent years,target detection has become a more mature research direction in the field of computer vision,which has been widely used in tasks such as scene graph generation and visual quizzing,and the research results can be used in military as well as medical fields.In order to enhance the detection accuracy of small targets in military target detection,the study of military target detection with hierarchical feature enhancement is carried out.This research improves the detection accuracy of small targets for military target detection from the perspective of making full use of hierarchical features.The specific work includes the following aspects.(1)To improve the accuracy of military target detection models,a Faster R-CNN-based military target detection method is proposed.The method uses the Faster R-CNN model for military target detection on the input image.The experiments on three major datasets show that the model can indeed effectively improve the task accuracy(2)To further solve the problem of low accuracy of small target detection in military target detection,a multilayer feature-enhanced military target detection method is proposed.The method uses a multilayer feature pyramid network to acquire multilayer features in the image,further enhances small target features,and uses a channel attention mechanism to model feature dependencies.Experiments show that the proposed method can indeed improve the detection of small targets.