结合视觉显著性和EfficientNetV2的舰船目标检测方法

Ship Target Detection Method Combining Visual Saliency and EfficientNetV2

随着光学遥感图像分辨率逐渐提高,对海面舰船目标快速精准检测成为海事研究的基本挑战之一。为了解决检测过程中面临的待检测图像尺寸大而目标稀疏、复杂环境干扰、目标提取时效性差、模型体积计算量大等问题,提出一种实用的舰船检测方案。引入视觉显著性有效加速预筛选过程,利用小波分解系数表达舰船目标区域与背景的差异,抑制噪声的同时增强目标方向特征,通过改进的四元数傅里叶变换相位谱模型(phase spectrum of quaternion Fourier transform,PQFT)生成显著图,并采用Gini指数引导多尺度显著图融合以增强图像尺度适应性及小目标显著性。与其他显著性方法相比,提出的模型能够有效抑制云、雾、海杂波、舰船尾迹等复杂环境的干扰,与经典的滑动窗口或其他区域建议方法相比产生更小的候选区域集合。得到显著图映射后,采用自适应阈值OTSU法对显著图进行二值分割。在目标判别阶段,利用轻量化网络EfficientNetV2有效剔除虚警。实验结果表明,所提出的船舶检测方法鲁棒性高,准确率高达96%,满足实时性需求。

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.