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As the development of object detection technology in computer vision, identifying objects is always an active yet challenging task, and even more efficient and accurate requirements are being imposed on state-of-the-art algorithms. However, many algorithms perform object box regression based on RPN(Region Proposal Network) and anchors, which cannot accurately describe the shape information of the object. In this paper, we propose a new object detection method called Field Network (FN) and Region Fitting Algorithm (RFA). It can solve these problems by Center Field. Center field reflects the probability of the pixel approaching the object center. Different from the previous methods, we abandoned anchors and ROI technologies, and propose the concept of Field. Field is the intensity of the object area, reflecting the probability of the object in the area. Based on the distribution of the probability density of the object center in the visual field perception area, we add the Object Field in the output part. And we abstract it into an Elliptic Field with normal distribution and use RFA to fit objects. Additionally, we add two fields to predict the x,y components of the object direction which contain the neural units in the field array. We extract the objects through these Fields. Moreover, our model is relatively simple and have smaller size, which is only 73 M. Our method improves performance considerably over baseline systems on DOTA, MS COCO and PASCAL VOC datasets, with overall performance competitive with recent state-of-the-art systems. [ABSTRACT FROM AUTHOR]

3D pose estimation is always an active but challenging task for object detection in remote sensing images. In this paper, we present a new algorithm for predicting an object's 3D pose in remote sensing images, called Anchor Points Prediction (APP). Compared to previous methods, such as RoI Transform, our object results of the final output can obtain direction information. We predict the object's multiple feature points based on the neural network to obtain the homograph transformation relationship between object coordinates and image coordinates. The resulting 3D pose can accurately describe the three-dimensional position and attitude of the object. At the same time, we redefine the method I o U A P P for calculating the direction and posture of the object. We tested our algorithm on the HRSC2016 dataset and the DOTA dataset with accuracy rates of 0.863 and 0.701, respectively. The experimental results show that the accuracy of the APP algorithm is significantly improved. At the same time, the algorithm can achieve one-stage prediction, which makes the calculation process easier and more efficient. [ABSTRACT FROM AUTHOR]