Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

Fan Xiangsuo,Xu Zhiyong

doi:10.1155/2019/9282141

Abstract

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

Highlights

Dim and small target detection algorithm is a critical technique for the imaging detection system. e system mainly combines the corresponding algorithms in the field of machine vision to automatically discriminate the targets in low signal-noise rate (SNR) scenes. e detection distance of the imaging detection system depends heavily on the quality of an algorithm [1]
Where T and TF represent the number of true frame and the total number of frames of the image, respectively; TR is the rate of true segmentation frame; and FR is the rate of failed segmentation frame
The detection algorithm proposed in this paper is compared with (TDLMS) [3], Top-Hat [24], improved bilateral filtering [25], and anisotropic filtering [26]

Summary

Introduction

Dim and small target detection algorithm is a critical technique for the imaging detection system. e system mainly combines the corresponding algorithms in the field of machine vision to automatically discriminate the targets in low signal-noise rate (SNR) scenes. e detection distance of the imaging detection system depends heavily on the quality of an algorithm [1]. Dim and small target detection algorithm is a critical technique for the imaging detection system. E system mainly combines the corresponding algorithms in the field of machine vision to automatically discriminate the targets in low signal-noise rate (SNR) scenes. It is difficult to accurately detect dim small target due to the lack of effective information such as shape and texture in the image. The detection algorithm based on single-frame filtering mainly refers to the preprocessing of single-frame images by means of filtering and learning classification algorithms in some signal processing fields, and acquiring corresponding candidate target points, and use some prior knowledge (such as pretrained model, template matching, etc.) to detect the real target points from numerous candidate targets. When the target is in a low SNR scene, using merely the single-frame information to detect dim-small targets in the above singleframe filtering detection algorithm may result in a high. Analysis. e algorithm proposed in this paper is compared with other algorithms, and contrast gain, signal-to-noise ratio gain and background inhibitor factor are used to evaluate the background modeling effect of different algorithms. e specific indicators are as follows [23]:

Methods

Results

Conclusion