Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions

In In the process of sea surface object detection by remote sensing images, this paper proposes an improved Faster R-CNN sea surface object detection algorithm for the problems of low accuracy and long detection time using deep neural networks. Firstly, the size of the bounding box in the region proposal networks is analyzed, and then the object in the image is clustered using the K-Means algorithm, and the clustering results are input into region proposal networks, thereby realizing the region proposal networks improvement of. Secondly, the Soft-NMS algorithm is used to screen the target candidate frame to obtain the detected sea objects. The experimental results show that the algorithm of this paper can detect the sea surface object of remote sensing images under complex sea conditions, and its mAP can reach 87.25%, which is an average increase of 3.75% compared with the commonly used detection methods.