Research On Improving The Detection Of Aircraft Target In Arbitrary Direction In Remote Sensing Image Based On Anchor-Free Method

Target detection in aircraft remote sensing images is a technology that uses computer vision and remote sensing technology to automatically detect and recognize aircraft targets in remote sensing images.This technology can be widely used in military and civilian fields.For example,in the military field,it can help the military to understand the situation of enemy fighters in time and improve military combat effectiveness;In the civil field,it can be applied to aviation traffic control,aviation safety monitoring and other aspects to improve aviation safety.Traditional aircraft target detection mainly relies on visual observation by professionals,which is time-consuming and labor-intensive,and there is a risk of human error in judgment.With the continuous development and progress of computer technology,it has become a reality to use computers to detect aircraft targets.Now,computer technology has been able to process a large amount of data and complex algorithms,making the target detection of aircraft remote sensing images more accurate and efficient.At present,target detection in aircraft remote sensing image is a technology based on depth learning algorithm.In the target detection of aircraft remote sensing image,the depth learning algorithm can automatically identify and extract the features related to the aircraft target,such as the shape,size,position and other information of the aircraft.Through the analysis and processing of these features,accurate detection and recognition of aircraft targets can be achieved.With the help of a large number of annotation data and high-performance computing resources,the deep learning algorithm can achieve a good balance between accuracy and efficiency.At the same time,due to the complexity of the appearance and attitude of the aircraft in the complex background environment,the use of depth learning algorithm for aircraft target detection has certain advantages.In the natural optical remote sensing image target detection,it is usually taken from a horizontal angle,and the target detection process is mainly to locate the target horizontal boundary frame by marking.However,remote sensing images are usually taken at a certain height and angle above the earth’s surface.The direction and scale of the target object in the remote sensing image are variable and the proportion is small.At the same time,the background information of the remote sensing image is more complex than that of the natural optical image.The target features are affected by complex background information,which makes the detection of specific targets in remote sensing images more difficult.At present,according to whether the anchor point is set,the aircraft target rotation detection algorithm in aircraft remote sensing image can be divided into two types: the method based on anchor point and the method without anchor point.The target detection method based on anchor points generates multiple boundary boxes of different sizes and proportions in advance at the anchor points,and realizes target detection through classification and regression.When the target object is labeled at a small scale,it is easy to produce large errors,especially it is difficult to determine whether the pixels near the bounding box belong to the target object.This paper takes the detection of aircraft target in any direction of remote sensing image as the research object.Aiming at the shortcomings of existing methods,an improved model of aircraft target detection in any direction of remote sensing image without anchor is proposed.The main research contents and results of this paper are as follows:(1)The advanced anchored rotation detector BBAVectors is used as the reference model to build a deep learning network framework to realize the detection of rotating targets in remote sensing images.At present,the detection algorithms based on anchor frame have achieved good results in rotation detection,but they rely on a large number of predefined anchor related fine hyperparameters,and need to spend a lot of calculation time in the training phase.Therefore,the anchor free frame detection algorithm came into being.The anchor free detection method reduces the impact of anchor related parameters on the overall detection performance by eliminating predefined anchor frames.Anchorless method has a significant effect on computational performance and efficiency,so the existing target detection without anchor frame is further studied.(2)In this paper,a new loss function based on the eight-parameter method is proposed for better regression of aircraft rotating targets in remote sensing images.After introducing the current popular five-parameter,six-parameter and eight-parameter rotary regression boxes,this paper makes a comparative analysis and finally selects the eight-parameter rotary regression box as the regression method of this paper.However,the problems of difficulty in training the eight-parameter model and unsmooth regression still exist.Therefore,the eight-parameter version of the loss function is designed to solve these problems.This loss function mainly starts from the deficiency of the original eight-parameter rotation frame regression loss function,and better returns the rotation target by changing the position relationship of each vertex and taking the minimum value of loss regression.In order to prove the effectiveness of the proposed algorithm,this paper carried out ablation experiments under the remote sensing data set DOTA data set.The experimental results show that the loss function based on the eight-parameter method proposed in this paper can effectively improve the accuracy and robustness of aircraft target detection.(3)In this paper,an improved target detection algorithm in any direction of remote sensing image without anchor is proposed to solve the problem of low detection accuracy caused by small size,dense distribution and complex background of aircraft targets.The algorithm uses BBAVectors as the benchmark model and Res Net50 as the backbone network for feature extraction.After the feature pyramid network FPN,a top-down path is added to expand the network PANet module,which shortens the information path and enhances the feature pyramid with low-level accurate location information.Secondly,this paper introduces the attention mechanism CBAM module,which improves the accuracy of aircraft target detection in complex environments by suppressing noise and highlighting target characteristics.In this paper,ablation experiments and contrast experiments were carried out on DOTA data sets,and the results showed that the detection accuracy of the improved model on color remote sensing image test data sets reached 90.35%.Compared with the original model,the detection accuracy is improved by 0.82%.These results show that the improved target detection algorithm in any direction of remote sensing image without anchor points proposed in this paper has high reliability and practicability,and can effectively improve the accuracy and robustness of aircraft target detection.