RDYOLOv5m6-KF: A Rotation Detector for Ship Detection in Remote Sensing Images

Sicong Chen, Chaobing Huang
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Abstract

The use of remote sensing images for ship detection can accurately monitor ship targets and provide reliable reference for monitoring key sea areas. Since the horizontal detection model cannot precisely locate and represent the specific direction of the ship, we propose a rotation detector based on YOLOv5m6 and KFIoU, which can realize the detection of ships in arbitrary orientations. On the other hand, the punishment based on Gaussian Wasserstein distance is used in model to generate confidence loss, which improves the discrimination between foreground and background during ship detection. Finally, transformer pyramid attention is added to the backbone of network, which uses the fusion of information extracted in multi-scale space and the self-attention mechanism to improve the feature extraction effect and the accuracy of detection. On FGSD2021 dataset, our model finally achieves 88.24% of mAP after adding attention mechanism and improving the confidence loss.
RDYOLOv5m6-KF:一种用于遥感图像船舶检测的旋转检测器
利用遥感图像进行船舶探测,可以准确监测船舶目标,为重点海域的监测提供可靠参考。由于水平检测模型不能精确定位和表示船舶的具体方向,我们提出了一种基于YOLOv5m6和KFIoU的旋转检测器,可以实现对任意方向船舶的检测。另一方面,在模型中使用基于高斯沃瑟斯坦距离的惩罚来产生置信损失,提高了船舶检测过程中前景和背景的区分能力。最后,在骨干网络中加入变压器金字塔关注,利用多尺度空间提取信息的融合和自关注机制,提高特征提取效果和检测精度。在FGSD2021数据集上,我们的模型在加入注意机制和改善置信度损失后,最终实现了88.24%的mAP。
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