基于YOLOv5的舰船目标及关键部位检测算法

为进一步提升对可见光图像中水面舰船目标的检测识别成功率, 提出一种基于YOLOv5的舰船目标识别算法。使用基于随机池化方法的空间金字塔池化网络, 运用双向特征金字塔网络进行多尺度特征融合, 采用指数线性单元函数作为激活函数加快网络训练收敛速度, 提升算法鲁棒性, 从而实现了对水面舰船目标和舰船关键部位的快速准确识别。通过在舰船目标及其关键部位数据集上实验验证, 对比多个经典目标检测方法, 在识别准确率上均有不同程度提升, 对比原YOLOv5s模型, 平均精度均值提升3.03%, 速度提升2 FPS, 模型保持了YOLOv5轻量化的特点, 在应用部署上有良好前景。

Ship target and key parts detection algorithm based on YOLOv5

In order to improve the detection and recognition success rate of the surface warship target in visible light images, an algorithm based on YOLOv5 is proposed. The spatial pyramid pooling network based on stochastic pooling is used for pooling operation, and the bi-directional feature pyramid network is used for feature fusion. At the same time, the exponential linear unit function is used as the activation function to further accelerate the convergence speed and improve the robustness of the model, so as to realize the rapid and accurate recognition of surface ship targets and key parts of the ship. Through the experimental verification on the data set of the ship target and its key parts, compared with the mainstream target detection methods, the recognition accuracy is improved in varying degrees. Compared with the original YOLOv5s model, the mean average precision is improved by 3.03%, and the speed is improved by 2 FPS. The model maintains the lightweight characteristics of YOLOv5 and has a good prospect in application deployment.