# Dim Staring Debris Targets Detection Method with Dense Long Trailing Star
## 2 Semi-Occluded Object Detection Algorithm Based on FCN
### Semi-Occluded Target Clustering Recognition Based on Edge Features  
After the tail aggregation calculation, the interference of noise on the stellar tail and among the stellar tail regions is eliminated. However, in the process of detecting the debris target, they will still be blocked by the star's trailing tail. This section will analyze several possible situations, mainly explain the features of the debris target under the occlusion in detail, and give the solution.  
Considering the star trailing width \(W_{f}=4\) and the fragment target size \(L_{d}=3\), when occlusion occurs, it can be divided into three types according to the occlusion size as in Figure 8.  
Case one: Occlusion is more than \(2/3\), which means that only one line of the debris target exposes the star trail. In this case, the debris target and the star trail are superimposed. Although there is local brightness accumulation, it is difficult to distinguish due to the noise;  
Case two: When the occlusion is between \(2/3\) and \(1/5\), there are more than two lines of star trailing. In this case, an obvious bump is formed at the edge of the trailing star, and the bump has light and dark changes, which is obviously different from the noise bump. A large number of sample extraction statistical features need to be formed by the fragment target extraction;  
Case three: When the occlusion is below \(1/5\), the debris target has only local contact with the stellar tail, and such occlusion is easier to distinguish on the basis of the rectangular fitting.  
The distribution of occluded targets is shown in Figure 9 as a Gaussian distribution, while the star trailing statistics are mainly Rayleigh distribution. Therefore, in cases two and three, debris targets can be distinguished according to different distributions.  
Figure 8: Classification of debris target occlusion.  
Figure 7: Fitting result of trailing rectangle (red, blue, yellow: targets separated in different frames).  
Figure 10 shows the common star trailing occlusion debris target scene, covering three types of occlusion cases. The left side of each scene shows a single gray image (a,c,e), and the right side shows three consecutive superimposed images (b,d,f).  
Figure 11 shows the image features of the occluded fragment target. It can be seen that the gray features of the fragment target are quite different from the stellar trail. In case one, when the object is completely occluded, we can also distinguish it according to the continuous frame image. It can be seen that although there are feature differences in occlusion, there are fewer feature pixels, and it is difficult to detect quantitatively by noise interference, which makes it impossible to distinguish rules. Although the statistical distribution difference is obvious, the fragment target pixels are too few to be directly extracted according to the statistical distribution difference.  
Figure 10: Object scene of common star trailing occluded debris (**a,b,f**) case 3: (**left**): single gray image; (**right**): three consecutive frames of superimposed image; (**c,d**) case 2: (**left**): single gray image; (**right**): three consecutive frames of superimposed image; (**e**) case 1: (**left**): single gray image; (**right**): three consecutive frames of superimposed image (red, blue, yellow: targets separated in different frames).  
Figure 9: Gray distribution of occluded debris.  
By focusing on these difficulties and problems, this section designs a small, fully connected convolutional network (FCN). By training the occluding fragment target samples, the edge \"bulge\" information after rectangular fitting is used as a suspected target for FCN calculation and finally determines whether it is a fragment target. The small FCN design is shown in Table 2, which contains six layers of convolution calculation. The final output result 0 represents the non-fragmented target, and 1 represents the fragmented target.