摘要
Abstract
With the increasing resolution of optical remote sensing images,fast and accurate detection of ship targets on the sea has become one of the basic challenges of maritime research.In order to solve the problems faced in the detection process,such as large image size but sparse targets,complex background interference,poor timeliness of target extraction,and large calculation of model volume,a practical ship detection scheme is proposed.Visual saliency is introduced to effectively accelerate the pre-screening process,and the difference between the ship target area and the background is effectively expressed by wavelet decomposition coefficients,which can enhance the target directional characteristics while suppressing noise.Saliency map is generated through the improved model based on phase spectrum of quaternion Fourier transform(PQFT).In addition,Gini index is exploited to guide multi-scale saliency image fusion to enhance image scale adaptability and small target saliency.Comparing with other saliency methods,the proposes model can effectively suppress the interference of complex environments such as cloud,fog,sea clutter,and ship wake.More importantly,it pro-duces a smaller set of candidate regions than the classical sliding window or other region recommendation methods.After the saliency map is obtained,the adaptive threshold OTSU method is employed for binary segmentation of saliency map.In the target discrimination stage,the lightweight network EfficientNetV2 is exploited to effectively eliminate false alarms.The experimental results show that the proposes ship detection method has high robustness and accuracy up to 96% ,meeting the real-time requirements.关键词
光学遥感/舰船检测/PQFT算法/视觉显著性/EfficientNetV2Key words
optical remote sensing/ship detection/phase spectrum of quaternion Fourier transform(PQFT)algorithm/visual saliency/EfficientNetV2分类
信息技术与安全科学
