船舶目标重叠下马赛克图像数据增强方法研究

Research on mosaic image data enhancement and detection method for overlapping ship targets

中文关键词  船舶识别  目标重叠  图像数据增强  Yolov4算法  深度学习

英文关键词  ship recognition  target overlap  image data enhancement  Yolov4 algorithm  deep learning

中文摘要

      目标识别中的重叠遮挡问题一直以来是研究的难点, 船舶目标在狭窄水域发生相互遮挡的情况依然存在. 本文提出了一种改进的马赛克数据增强方法, 将训练图片拼接变换成3种不同的尺度, 并按照不同比例输入网络进 行训练, 强化了检测算法对局部特征的学习能力, 在保持测试速度不变的情况下, 提高了对重叠目标的识别准确率, 降低了不同分辨率下识别能力的衰弱速度, 加强了算法的鲁棒性. 基于小型移动测试平台的实测实验证明, 相对于 原始算法, 经过改进后的算法在重叠目标的识别准确率上提高了2.5%, 目标丢失时间减少了17%, 在不同视频分辨 率下的识别稳定性上提高了27.01%.

英文摘要

      The problem of overlapping occlusion in target recognition has been a difficult research problem, and the situation of mutual occlusion of ship targets in narrow waters still exists. In this paper, we proposed an improved mosaic data enhancement method, which transforms the training image stitching into three different scales and inputs them into the network for training at different scales to strengthen the learning ability of the detection algorithm for local features. While keeping the test speed constant, the recognition accuracy of overlapping targets is improved, the rate of decay of recognition ability at different resolutions is reduced, and the robustness of the algorithm is enhanced. The experiments based on a small mobile testbed proved that, compared to the original algorithm, the improved algorithm improved the recognition accuracy of overlapping targets by 2.5%, reduced the target loss time by 17%, and improved the recognition stability at different video resolutions by 27.01%.