边缘信息引导目标完整性增强的显著性目标检测算法OA

In the saliency object detection task,a salient object detection method based on object integrity enhancement guided edge information is proposed to address the problems of blurred edges and object incompleteness in recognition results.Firstly,the diversity feature extraction module was proposed to capture the features of complex and variable salient objects through various convolutional operations,thereby enriching the feature representation of the model.Then,the object integrity enhancement module was designed to process the initial fused multi-level features in parallel,and the integrity information of salient objects was further enhanced by exploring spatial and channel branches.Finally,the edge feature enhancement module was employed to use the deep edge prediction features to guide the feature map to pay more attention to the foreground and background region and edge information,and to improve the model's edge perception capability.Experiments on four public datasets,such as ECSSD and DUTS-TE,showed that the proposed algorithm achieved higher detection accuracy than other advanced algorithms in several metrics,such as S-measure and F-measure on DUTS-TE dataset were 0.859 and 0.895,respectively.The proposed algorithm demonstrated superior capability in the perception and refinement of salient object boundaries,further enhancing its robustness in complex scenes.