# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 3 Experiment and Comparison
### Real-Time Performance Comparisons  
For the input of a single frame of 10,240 \(\times\) 10,240 pixel image, Yolo Tiny V2 and V3 chose 416 \(\times\) 416 pixel input. Considering the overlap of 16 pixels between image slices (the target was not more than 150 m), a single image frame needed to be calculated 625 times. In this paper's algorithm design, the central computation time was spent in the preprocessing, and the data volume processed by the subsequent feature discrimination, association discrimination, and S-YOLO discrimination was less than 1% of the preprocessing. The computing platform of this algorithm was a i7-10700k processor, NVIDIA GeForce RTX 3060 graphics card, with 16 GB DDR4 memory, CUDA 11.1, and MATLAB R2018a. Figure 8: Calculation results in the performance of different discrimination steps of the design method in this paper. Figure 9: Performance comparison of different numbers of association frames and association discrimination results designed in this paper. In the pre-processing calculation process, \(\beta\) adopted the all-1 structure with the size of 3 \(\times\) 3, and the iteration number \(R\) was selected as 3, then the calculation time of morphological reconstruction was equivalent to completing three image reading and storage operations. The calculation time was proportional to \(M\times N\times R/f_{val}\) (\(f_{val}\) representing the effective main frequency of calculation, with an average of 2 GHz). The computation time of adaptive threshold segmentation was equivalent to one image reading and storage, and the computation time was proportional to \(M\times N/f_{val}\). The calculation time of connected domain label was calculated by fast label, and the time was proportional to \(2M\times N/f_{val}\). In the process of feature identification, the number of first-level suspected targets \(P^{\prime}\) in a single image was between 200 and 1000, and 1000 was selected. Since the input slice size of first-level suspected targets was 52 \(\times\) 52, the calculation time of simplified S-HOG algorithm was proportional to \(314\times 52\times 52\times P^{\prime}/f_{val}\), and the calculation time of binary classification SVM was proportional to the number of feature vectors, namely \(1044\times 288\times P^{\prime}\). 314 and 288 were statistical values. In the process of correlation authentication calculation, the input number of second-level suspected targets \(P^{\prime\prime\prime}\) ranged from 20 to 100, \(P^{\prime\prime\prime}\) was selected as 100, and the time was mainly spent in searching the circular area with \(r\) as the radius of the front and back frames. Considering that the motion of the front and back frames did not exceed 100 pixels, \(r\) was selected as 64, then the calculation time of correlation authentication was proportional to \(255\times P^{\prime\prime}\times\pi\times r^{2}/f_{val}\). In the calculation process of S-Yolo authentication, the number of second-level suspected targets \(S\) was generally no more than 10, and the number of sequence sets in the authentication process was 3. According to the network structure in Table 1, it can be seen that the whole calculation time was proportional to \(44\times 10\times S\times 23296\), in which 44 was the statistical coefficient. Table 4 shows the calculation time of four steps and each sub-step in the actual test process, and the total calculation time was 1.7553 s. Table 5 shows the calculation time comparison between this design and previous methods. Considering the literature [7; 8], only for less than 512 \(\times\) 512 pixels below the calculation of the image, this design input 10,240 \(\times\) 10,240 pixels. This design on computing time was less than 35% of the reference, and the method of design greatly improved the efficiency of target detection.