Constraints often exist in the high-dimensional data output in object detection, such as the inverse vector (cos θ, sin θ) of the two-dimensional object and the attitude quaternion of the threedimensional object. The range of each component of the output value of the traditional neural network is unconstrained, which is difficult to meet the needs of practical problems. To solve this problem, this paper designed the transformation network layer according to the high dimensional space transformation theory and constructed a constrained neural network model to detect the pose of objects from a single aerial image. Firstly, in the yolov3 network structure, according to the size of the three field scales, three scale transformation network layers are added correspondingly to implement the constrained unit quaternion field. Secondly, according to the characteristics of quaternion, we proposed a special loss function. Then a new constrained neural network called the quaternion field pose network (qfiled PoseNet) model is constructed, which can predict the probability field of the object and the contained unit quaternion field respectively. Next, The object’s probability field is generated to determine the 2D bounding box of the object, and the unit quaternion field is generated to determine the 3D rotation R. Finally, combining the rotation matrix R and the 2D bounding box of the object to calculate the 3D translation T. We used our method to experiment on DOTA1.5 data set and the HSRC2016 data set respectively. The experimental results show that our method can detect the pose of the object well.

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.

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 $Io{U}_{APP}$ 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.