Multiframe Detection of Sea-Surface Small Target Using Deep Convolutional Neural Network
Liwu Wen, Jinshan Ding, Zhong Xu
Abstract
Sea-surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multiframe detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential range–Doppler (RD) spectra. A two-step detection framework is proposed, where the intraframe and interframe detections are achieved using the differences in features and interframe correlations between the moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multiframe detection algorithms. Additionally, this approach exhibits acceptable generalization ability.