Military Object Detection Based on Optimal Gabor Filtering and Deep Feature Pyramid Network

Military object detection technology plays an important role in realizing the informatization and intelligence of military equipment, but the complex environment and scarce sample data of military objects also become the difficulties of detection. This paper proposes a detection framework of military objects based on optimal Gabor filtering and deep feature pyramid networks. At first, we combine the texture characteristics of military objects with the requirements of detection tasks and proposed the Fine Region Proposal Network (FRPN). A set of Gabor filter is designed and screened in this scheme, we construct the optimal Gabor filter Banks by analyzing the image energy after Gabor transformation of some images in the dataset, shorting the time of feature extraction and reducing the amount of calculation. Then, the Renyi threshold segmentation method is adopted to obtain the region proposals. Finally, the Highly Utilized Feature Pyramid Networks (HU-FPN) is proposed to improve the detection effect of small objects. A bottom-up and a top-down feature pyramid is constructed in the stage. Through transverse connection and integration of features at various scales, the feature expression of small objects is enriched and the detection problem of small objects is effectively solved. The experimental results show that the method proposed in this paper has prominent advantages in accuracy, effectiveness and small object detection when compared with the state-of-the-art method, which can create good conditions for the realization of rapid and accurate detection of military objects and precise strike under military background.

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