Infrared Small Target Detection Of Nuclear Research Support Vector Machine (SVM) Method

With the advancement of science and technology,artificial intelligence is gradually applied to various occasions,especially in the direction of image processing,and it has made great progress.An infrared image is a series of grayscale images captured by a thermal sensor camera.The higher the heat of the target,the greater the grayscale value it displays in infrared images.In general,targets with extremely high heat,such as planes and missiles,usually appear as bright spots in infrared images,while the gray value of clutter on the cloud background is relatively low.Thus the signs of movement of planes and missiles can be clearly seen.However,when the target is far from the camera,or when the target overlaps with the cloud background,its gray value is not much higher than the cloud background,and the shape of the target is small,which is difficult to notice by the human eye.Moreover,manually observing the small target of the infrared image requires a great deal of manpower,and it cannot achieve 24-hour real-time monitoring.The accuracy and real-time performance are not satisfactory.Therefore,the automatic detection technologies of dim and small targets in infrared images emerge from time to time,and more and more methods are used to detect small targets.The best algorithms in image recognition are usually deep learning algorithms such as convolutional neural networks(CNN),long-short memory networks(LSTM),and so on.However,the deep learning algorithms can not usually be used to identify the small target of the infrared image,because the small target only occupies a very small area in the entire image and has little effect on the entire image.But the deep learning algorithms usually use the entire image as input for training data.Therefore,it is difficult to accurately identify the small target directly using the deep learning algorithm.In addition,the deep learning algorithms also requires a lot of time,then can not meet the real-time requirements of the small target recognition.In the other hand,the traditional small target recognition algorithms are usually based on the enhancement of the contrast of the small target area.After that,some image segmentation algorithms such as threshold segmentation are used to extract the small target area.This method can be applied to most cases,but when the gray value of the small target is lower than the gray value of the cloud clutter,the conventional method is usually difficult to identify correctly.In order to solve the above problems,this paper proposes a small target recognition method that combines the traditional image enhancement algorithm and machine learning algorithm.It includes the advantages of the two types of algorithms.While ensuring the accuracy of small target recognition,the false alarm rate of small target recognition is reduced.That means the cloud background is rarely recognized as a small target.The algorithm of this paper is divided into three steps: First,the LoG convolution function is used to convolute the infrared image that originally contains the small target,to enhance the contrast of the small target region;Second,the threshold segmentation algorithm is used to extract some 11*11 pixel regions that may be small targets,which needs to set a lower threshold;Third,use kernel support vector machine(KSVM)to identify the regions extracted in the previous step,and finally determine the location of the small target.This algorithm avoids inputting large amount of useless background data into KSVM,which greatly improves the running speed of the algorithm.When KSVM determines the small target area in the last step,it can greatly reduce the false alarm rate and improve the recognition performance of the algorithm.The experimental results of the four groups of infrared images show that the false alarm rate is far lower than the traditional algorithm while the algorithm guarantees the correct rate.