Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur's entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.