一种基于特征重用金字塔的舰船检测算法

针对现有算法在SAR图像舰船目标检测场景中难以提取模糊目标特征的问题,提出一种基于特征重用金字塔的舰船目标检测算法。所提算法以YOLOV4-tiny为主体,首先将线性因子引入到K-means算法中整合初始锚框,加强网络对多尺度目标的适应性;其次在主干CSPDarknet53-tiny中添加注意力机制来抑制干扰信息,减弱复杂背景的影响;最后利用特征重用机制强化特征金字塔,提升网络对模糊目标特征的提取能力。实验结果表明,相较于YOLOV4-tiny网络,改进后的算法在SSDD数据集上的平均检测精度提升11.79%,证明了改进后算法在舰船检测中的有效性。

A Ship Detection Algorithm Based on Feature Reuse Pyramid

Aiming at the problem that the existing algorithms are difficult to extract fuzzy target features in the SAR image ship target detection scene, a ship target detection algorithm based on feature reuse pyramid is proposed. The proposed algorithm takes YOLOV4-tiny as the main body. First, a linear factor is introduced into the K-Means algorithm to integrate the initial anchor frame to enhance the adaptability of the network to multi-scale targets. Secondly, an attention mechanism is added to the backbone CSPDarknet53-tiny to suppress interference. information, and weaken the influence of complex background; finally, the feature reuse mechanism is used to strengthen the feature pyramid and improve the network''s ability to extract fuzzy target features. The experimental results show that, compared with the YOLOV4-tiny network, the average detection accuracy of the improved algorithm on the SSDD dataset is improved by 11.79%, which proves the effectiveness of the improved algorithm in ship detection.