基于改进DeeplabV3+的水面多类型漂浮物分割方法研究OA北大核心CSTPCD

[Objective]In order to solve the problems of poor robustness of traditional image processing method and the inability of commonly used deep learning detection method to accurately recognize the boundaries of large floating objects,[Methods]a se-mantic segmentation method based on the improvement of DeeplabV3+for the recognition of multiple types of water surface floats was proposed,which improves the recognition ability of water surface floats.By classifying the collected actual water surface floats into categories,a homemade dataset is used for comparison experiments.The algorithm selects the xception network as the backbone network to obtain preliminary float features,introduces the attention mechanism in the enhanced feature extraction net-work part to emphasize the effective feature information,and incorporates the fully-connected conditional random field model in the post-processing stage to fuse the local information of a single pixel with the global semantic information.[Results]Comparing the image segmentation performance metrics,the improved algorithm mPA(Mean Pixel Accuracy)is improved by 5.73％and mIOU(Mean Intersection Over Union)is improved by 4.37％.[Conclusion]Compared with other algorithmic models,the im-proved DeeplabV3+algorithm is more capable of acquiring features of floating objects,and at the same time obtains rich detail in-formation to more accurately identify the boundaries of multiple types of water surface floating objects and more difficult to clas-sify floating objects,which meets the needs of floating object detection in the actual water environment after testing on multi-ple reservoir scenarios.