复杂场景下自适应特征融合的多尺度船舶检测

Multi-scale ship detection based on adaptive feature fusion in complex scenes

受台风、大雾、雨雪等复杂天气以及遮挡、尺度变化等影响,现有船舶检测方法存在误检和漏检问题。针对上述复杂场景问题,在YOLOX-S模型的基础上,提出一种自适应特征融合的多尺度船舶检测方法。首先,在主干特征提取网络中引入特征增强模块,抑制复杂背景噪声对船舶特征提取的干扰;其次,考虑深浅层次特征融合比例问题,设计自适应特征融合模块,充分利用深浅层次特征,提高模型的多尺度船舶检测能力;最后,在检测头网络,将检测头解耦,并引入自适应的多任务损失函数,平衡分类任务和回归任务,提高船舶检测的鲁棒性。实验结果显示,所提方法在公开船舶检测数据集SeaShips和McShips上的检测平均精度均值(mAP)分别达到了97.43%和96.10%,检测速度达到每秒189帧,满足实时检测的要求,验证了所提方法在复杂场景下仍能对多尺度船舶目标实现高精度检测。

Under the influence of complex weather such as typhoon,heavy fog,rain and snow,as well as occlusions and scale changes,the existing ship detection methods have the problems of false detection and missed detection.In order to solve the above complex scene problems,based on YOLOX-S model,a multi-scale ship detection method based on adaptive feature fusion was proposed.Firstly,a feature augmentation module was introduced into the backbone feature extraction network to suppress the interference of complex background noise on ship feature extraction.Then,considering the problem of deep and shallow feature fusion proportion,an adaptive feature fusion module was designed to make full use of deep and shallow features,thereby improving the multi-scale ship detection ability of the model.Finally,in the detection head network,the detection head was decoupled and an adaptive multi-task loss function was introduced to balance classification tasks and regression tasks,thereby improving the multi-scale ship detection robustness of the model.Experimental results show that the detection mean Average Precision(mAP)of the proposed method on the public ship detection datasets SeaShips and McShips is 97.43% and 96.10%,respectively.The detection speed of the proposed method reaches 189 frames per second,which meets the requirements of real-time detection,demonstrating that the proposed method achieves high-precision detection of multi-scale ship targets even in complex scenes.