Abstract
In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.
Highlights
Photoelectric imaging detection systems obtain information on the target object by detecting reflected or radiant energy from the object’s surface
Background modeling methods mainly include two-dimensional least mean square (TDLMS) filtering [1], adaptive Butterworth filtering [2], improved Top-Hat filtering [3], improved bilateral filtering [4], direction support vector filtering [5], and background modeling methods based on statistical characteristics [6]. ese algorithms work by using filtering algorithms to perform background estimation on the scene image before differentiating the estimated background image from the original image, obtaining a differential image that contains only the target object and a small amount of noise
In order to evaluate the performance of the detection algorithm proposed in this paper, we selected four dynamic scenes for experimentation
Summary
Photoelectric imaging detection systems obtain information on the target object by detecting reflected or radiant energy from the object’s surface. For dynamic scenes with low signal-to-noise ratios, it is possible to achieve the detection of dim and small target objects by first using preprocessing algorithms to remove most of the interference on the target caused by noise before applying multiframe motion correlations. E present paper proposes an algorithm based on spatiotemporal motion characteristics that improve the detection of dim and small target objects in dynamic scenes. It is difficult to preserve the edge contour in the background modeling process, resulting in much edge noise in the differential image, which is not conducive to extracting target points [22] To effectively solve this situation, the differential filter function above is improved. Erefore, according to the above characteristics, the mean value of three directions with smaller diffusion function value can be selected for differential filtering, which will effectively enhance the target signal. Output filter result Figure 2: e flow chart of improved differential filtering
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