Ship Target Detection Algorithm Based on Decision-Level Fusion of Visible and SAR Images
Jianlai Chen, Xiaoqing Xu, Junchao Zhang, Gang Xu, Yucan Zhu, Buge Liang, Degui Yang
Abstract
Aiming at the problem of target detection for multiple source information fusion, in this article, a decision-level fusion algorithm for visible and SAR images is proposed. First, using the Faster-RCNN network detects visible and SAR images to retain the detection results, respectively. Second, the semantic segmentation of visible images based on U-Net is realized. Finally, based on the detection results of single source and semantic segmentation results of land and sea, a fusion strategy based on decision level is proposed to achieve accurate target detection under multisource information. Through experimental verification, the detection performance of the proposed algorithm is an advantage over that of single-source image detection. The detection accuracy is 2.87% and 4.73% higher, and the recall rate is 3.02% and 0.19% higher than that of visible and SAR images separately. Compared with other target detection algorithms based on traditional image fusion, the proposed method has fewer false detections and missed detections.