梯度纹理直方图与多层感知器船舶快速检测

在动态的复杂环境中，受背景建模失效影响，传统船舶目标检测方法的精度较低。针对该问题，提出一种基于梯度纹理直方图特征与多层感知器的船舶快速检测算法。该算法利用多层感知器将目标的梯度与纹理的直方图进行特征融合，为船舶目标构建特征空间。首先，基于二值梯度的特征训练船舶候选区模型，以快速生成具有高召回率的少量船舶候选窗口，并在每个候选窗口提取梯度纹理直方图特征；其次，设计一个多层感知器作为船舶分类器，对提取到的梯度纹理直方图特征进行判别。实验结果表明，该算法在多个海上场景中船舶检测平均精确率达90.0%，平均执行时间为20.4 ms/frame，有效实现海上船舶精确与快速的检测。

Rapid ship detection based on gradient texture features and multilayer perceptron

Aiming at the issues of low ship detection rate caused by the failure of background modeling in the dynamic complex environment of traditional ship detection methods, a rapid ship detection algorithm based on gradient texture histogram features and multilayer perceptron was proposed. The feature fusion between gradient and texture histogram of the target was performed using multilayer perceptron, constructing the feature space for ship targets. Firstly, the region proposal model based on binarized normed gradient feature was trained to quickly generate a small number of ship candidate windows with high recall rate and then the gradient texture histogram features were extracted from each candidate window. Secondly, a multilayer perceptron was designed as a ship classifier to distinguish the gradient texture histogram features. Experimental results show that the proposed algorithm has an average precision of 90.0% and an average time of 20.4 ms/frame in multiple maritime scenes, which effectively realizes rapid ship detection in maritime scenes.