*3.5. Qualitative Evaluation*

The qualitative results of the M-O SiamRPN with multi-optimization and state-of-theart methods are compared in Figure 12. The left column shows pairs that were correctly localized by all frameworks, and the right column shows pairs that were successfully localized by M-O SiamRPN with multi-optimization but failed to be localized by other frameworks. It can be seen that all methods perform well when obvious features (e.g., bright colors, special edges) appear in the image. In contrast, when the target in the search image is blurred or has similar contours to the surroundings, other localization methods are prone to mislocalization or missed detection, while our framework can still localize correctly. The improvement in localization performance can be attributed to the following aspects. Firstly, the fusion of the second-order covariance information enables the backbone CNN to extract the target features more effectively and characterize the blurred edges more adequately. Secondly, the negative impact of information imbalance on the classifier is reduced due to the integration of weight adaptive scale into the classification branch loss function. Finally, the intersection over union constrains the anchor boxes from different aspects, resulting in more accurate localization of the location regression branches.

**Figure 12.** Qualitative comparison of M-O SiamRPN with multi-optimization and state-of-theart frameworks.