## 4 Results and Analysis
At the same time, with the increase of the depth of the UUV, the background areas of KIF, SFBA and SSFAD become rougher, indicating that the increase in the depth of water will aggravate the mixed pixel phenomenon, which is unfavorable in the target detection. Meanwhile, Figure 9c',g' show that the background and target of VABS and LRX become indistinguishable at the water depth of 5 m, indicating a failure in detection efficiency. In addition, the size of the peak value of the target region detected by most algorithms begins to weaken with the increase of water depth, indicating that the distinction between the underwater target and water decreases, which is also detrimental to the recognition of underwater targets. The weakening of water depth on target detection performance is also verified in Table 3. For example, when the water depth is 0.1 m, all the algorithms successfully detect underwater targets except SFBA. However, when the water depth is 5 m, the VABS, SFBA, SSFAD, LRX, and LRASR all fail in the underwater target detection. Table 3 also shows that the algorithms proposed in our work achieve the best performance in the comparison of the FNR and FPR, and the SCR is also close to the best performance of similar algorithms, which demonstrates the advantages and strong detection performance of the proposed algorithm. The test environment created in this work for a comparative analysis of the efficiency achieved by these algorithms is the Intel(R) Xeon(R) processor with a main frequency of 3.60 GHZ and an internal storage of 32 G. The software environment is MATLAB R2019b. To begin with, the computation is repeated five times for each experimental image, with the average time taken for each computation as the final result. As shown in Table 4, the algorithm proposed in this work leads to a significant improvement compared with those of Kernel IF, SSFAD, SRX_LOCAL and LRASR, which makes the processing faster. roughly over 16-60 times It is comparable with VABS, but slightly outperformed by SFBA and Global RX. This algorithm can be refined to further improve computational efficiency. As can be seen above, this algorithm improves accuracy on the basis of ensuring efficiency. Compared with other mainstream ones, the algorithm proposed in this work performs better in balancing accuracy and efficiency. Figure 6: Results of experiment 1: (**a**) A red box indicating the location of the underwater target for test on the image of visible light in Region 1. (**b–i**) show results of the algorithm proposed in this study, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. (**b’–i’**) show a partial 3D view of the surrounding region of the underwater target detected by the presented method, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. Figure 7: Results of experiment 2: (**a**) A red box indicating the location of the underwater target for test on the image of visible light in Region 1. (**b–i**) show results of the algorithm proposed in this study, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. (**b’-i’**) show a partial 3D view of the surrounding region of the underwater target detected by the presented method, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. Figure 8: Results of experiment 3: (**a**) A red box indicating the location of the underwater target for test on the image of visible light in Region 1. (**b–i**) show results of the algorithm proposed in this study, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. (**b–i**?) show a partial 3D view of the surrounding region of the underwater target detected by the presented method, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. Figure 9: Results of experiment 4: (**a**) A red box indicating the location of the underwater target for test on the image of visible light in Region 1.