一种基于Attention-YOLOv3的海面舰船目标检测的方法

近年来，基于深度学习的视觉检测方法在海面舰船目标检测领域中的应用愈加广泛。为了解决传统视觉检测方法检测精度不高，对小目标检测效果不好的问题，提出了一种基于Attention-YOLOv3的海面舰船目标检测方法，有效提高了对舰船目标的检测性能。在对主流的One-stage与Two-stage模型结构及特点的调研分析的基础上，利用YOLOv3的特征提取网络Darknet-53来获取图像特征，通过特征金字塔网络（FPN）网络结构融合特征提取网络中深浅层的语义信息，并添加注意力机制模块来进一步优化网络性能。将改进后的Attention-YOLOv3模型应用到海面舰船检测场景中进行验证，基于搜集到的舰船目标制作成COCO格式的数据集进行训练，使用包含海面舰船目标的图片作为测试集进行测试。实验结果表明，改进后的Attention-YOLOv3网络对比原检测网络模型，解决了小目标检测不敏感的问题，达到了更高的检测效果。

Method of Surface Ship Target Detection Based on Attention-YOLOv3

In recent years, the visual detection method based on deep learning has been widely used in the field of ship target detection on the sea surface. In order to solve the problems of low detection accuracy and poor detection performance for small targets in traditional visual detection methods, a sea surface ship target detection method based on Attention-YOLOv3 is proposed, which effectively improves the detection performance of ship targets. Based on the research and analysis of the structure and characteristics of mainstream One-stage and Two-stage models, YOLOv3''s feature is used to extract network Darknet-53 for obtaining image features, and the deep and shallow semantic information in the network is extracted through the fusion of features in the Feature Pyramid Network (FPN) network structure, and attention mechanism modules are added to further optimize network performance. The improved Attention-YOLOv3 model is applied to the verification of ship detection scenarios on the sea surface. It is trained based on the collected data set of ship targets in COCO format, and tested using images containing ship targets on the sea surface as the test set. The experimental results show that the improved Attention-YOLOv3 network solves the problem of insensitivity to small object detection and achieves higher detection performance compared to the original detection network model.