基于模板匹配和深度学习的港口舰船检测识别方法

A method for ship detection and recognition in port area based on template matching and deep learning

针对港口内舰船目标的特征与港口内陆上目标特征相近导致检测困难的问题,提出了一种基于模板匹配和深度学习的港口舰船检测识别方法。对于待检测的港口图像,为其制作模板图和二值模板图。对待识别的港口图像和模板图利用SIFT特征进行配准,配准后利用RANSAC算法计算仿射变换矩阵,进而将二值模板图投影到待识别的港口图中,从而将待识别的港口图像中的陆地部分屏蔽实现海陆分割。海陆分割后,利用Sobel算子提取候选目标。最后,本文利用VGGNet-16的深度学习模型对检测出的候选目标进行识别,从而将候选目标分区为军用和民用,并将非船目标剔除。本文方法在测试样本上检测率为93.22%,识别率为96.23%,高于通用目标检测框架Faster-RCNN和YOLO-v3。

Aiming at the problem that the characteristics of ship targets in the port are close to the characteristics of onshore targets in the port, a method for ship detection and recognition in port area based on template matching and deep learning is proposed. For the port image, a template map and a binary template map are created. The port image and the port template map are registered by SIFT feature, and the affine transformation matrix is calculated by the RANSAC algorithm after registration. And then the binary template map is projected into the port image so that the land in the image can be shield. After the sea-land segmentation, the Sobel gradient operator is used to extract the candidate targets. Finally, this paper uses the VGGNet-16 deep learning model to classify the detected candidate targets, and divides the candidate targets into military and civilian applications, and eliminates the false alarm targets. The detection rate and recognition rate of the method in this paper reaches 93.22% and 96.23%, which is higher than the general target detection frameworks Faster-RCNN and YOLO-v3.