The methods mentioned above mostly adopted the traversal search method possessing redundant calculation and cannot deal with the complex and changeable environment of remote sensing images. In addition, a small number of potential candidates with high scores are found by a multi-cascaded linear model. analyzed the possibility of covering ships by rotated bounding boxes. applied the appearance structure to guide the sampling process for the selective search. Aiming to capture possible object locations, Uijlings et al. The frequently used tool of selective search-based methods is segmentation which applies the similarity-merging strategy to obtain large areas. The cascading support vector machine (SVM) is then applied to complete the extraction process of candidate regions. used a sliding window to generate windows of different sizes and aspect ratios and extracted the visual features for each window. The feature classification-based methods usually extract the sliding window features first, and then certain classifiers are designed to predict the sliding image patches. generated a ship shape library based on the Hough transform and used the sliding window method to calculate the feature similarity between each window region and shape library. First, template matching is used to find the candidate areas of aircraft, and then principal component analysis (PCA) and a kernel density function are used to identify each area. proposed an aircraft detection method from coarse to fine. Some methods adopted the idea of template matching and match the candidate feature with the template library of objects. utilized the active contour method by constructing and minimizing the energy function. To separate the sea surface, Antelo et al. This strategy is widely used for some representative applications, including segmentation of ocean and land for ship detection and airport detection for aircraft detection. From the human perception of the object location, some methods learned the prior knowledge of candidate regions. Typical strategies were prior region uses, template matching, feature classification, selective search, etc. Finally, the object categories were determined by certain classifiers. Usually, candidate regions were first extracted, and then the features were manually designed for the objects. The early object-detection algorithms for optical remote sensing images were mostly based on manually designed features. The experimental results demonstrate the effectiveness of the proposed EFNet for both multi-category datasets and single category datasets. The best results of the proposed EFNet are obtained on the HRRSD with a 0.622 A P score and a 0.907 A P 50 score. Three remote sensing object-detection datasets, namely DIOR, HRRSD, and AIBD, are utilized in the comparative experiments. The FCF is mainly used to learn the candidate object knowledge based on the channel attention and the spatial attention, while the RCF mainly aims to predict the refined objects with two subnetworks without anchors. The EFNet consists of two eagle-eye fovea modules: front central fovea (FCF) and rear central fovea (RCF). Inspired by the mechanism of cascade attention eagle-eye fovea, we propose a new attention mechanism network named the eagle-eye fovea network (EFNet) which contains two foveae for remote sensing object detection. However, some common problems, such as scale, illumination, and image quality, are still unresolved. A great many works have achieved remarkable results in this task. Object detection possesses extremely significant applications in the field of optical remote sensing images.
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