Research on Mosaic Image Data Enhancement for Overlapping Ship Targets
This addresses overlapping occlusion in ship target recognition for maritime surveillance applications, representing an incremental improvement.
The paper tackles the problem of overlapping ship target recognition in narrow waters by proposing an improved mosaic data enhancement method, which increases overlapping target recognition accuracy by 2.5%, reduces target loss time by 17%, and improves recognition stability under different video resolutions by 27.01% while maintaining test speed.
The problem of overlapping occlusion in target recognition has been a difficult research problem, and the situation of mutual occlusion of ship targets in narrow waters still exists. In this paper, an improved mosaic data enhancement method is proposed, which optimizes the reading method of the data set, strengthens the learning ability of the detection algorithm for local features, improves the recognition accuracy of overlapping targets while keeping the test speed unchanged, reduces the decay rate of recognition ability under different resolutions, and strengthens the robustness of the algorithm. The real test experiments prove that, relative to the original algorithm, the improved algorithm improves the recognition accuracy of overlapping targets by 2.5%, reduces the target loss time by 17%, and improves the recognition stability under different video resolutions by 27.01%.