Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network

Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network

Blog Article

High-resolution optical remote sensing data can be utilized to investigate the human behavior and the activities of artificial targets, for example ship detection on the sea.Recently, the deep convolutional neural network (DCNN) in the field of deep learning is widely used in image processing, especially in click here target detection tasks.Therefore, a complete processing system called the broad area target search (BATS) is proposed based on DCNN in this paper, which contains data import, processing and storage steps.In this system, aiming at the problem of onshore false alarms, a method named as Mask-Faster R-CNN is proposed to differentiate the target and non-target areas by introducing a semantic segmentation sub network into the Faster R-CNN.

In addition, we propose a DCNN framework named as Saliency-Faster R-CNN to deal with the problem of multi-scale ships detection, which solves the problem of missing detection caused by the inconsistency between large-scale targets and training samples.Based on these DCNN-based methods, the BATS system is tested to verify that our system can integrate different ship detection methods to effectively solve the problems that existed in click here the ship detection task.Furthermore, our system provides an interface for users, as a data-driven learning, to optimize the DCNN-based methods.