尺度适应性感受野的船舶目标检测方法

Ship object detection based on scale-adaptive receptive field

现有船舶目标检测算法大部分只是基于传统目标检测算法的优化改进,没有考虑船舶具有尺度长宽比例的外观特性,在多尺度目标检测中出现漏检误检问题。为了解决此问题,在YOLOXs基础上,提出一种尺度适应性感受野的船舶检测方法(SAF-YOLOX)。首先,对主干网络提取的不同特征层通过构建双向特征金字塔进行特征融合,增强每个尺度下的特征描述力;同时,设计自适应特征强化模块,抑制不同尺度的特征融合引入的冗余信息,弱化背景信息;然后在预测时,采用多路并行感受野的检测头,利用具有适应目标大小以及比例的感受野提取目标尺度适应性特征信息进行预测;最后,采用先筛选再分配的收敛感知策略,根据网络的收敛状态动态地分配样本,保证检测速度的同时提高检测精度。实验结果显示,所提方法在大型海事监控数据集SeaShips和MCShips上的平均检测精度分别达到93.21%和92.34%,与传统YOLOXs相比,分别提高了1.01%和1.09%。实验结果证明,所提方法利用尺度适应性感受野能实现多尺度船舶目标的高精度检测。

The existing ship object detection algorithms mostly rely on optimized improvements based on traditional object detection algorithms,without considering the scale and aspect ratio characteristics of ships,leading to issues such as missed detections and false detections in multi-scale object detection.To address this,the paper proposed a scale-adaptive receptive field ship detection method(SAF-YOLOX)based on YOLOXs.Firstly,it extracted different feature layers by the backbone network,which were fused by constructing a bidirectional feature pyramid,improving feature representation at various scales.Simultaneously,it designed an adaptive feature enhancement module to suppress redundant information introduced by the fusion of features at different scales,thereby attenuating background information.During the prediction phase,it employed a multi-branch parallel receptive field detection head,utilizing receptive fields adapted to target sizes and proportions for extracting scale-adaptive feature information.Additionally,it implemented a convergence-aware strategy,dynamically selecting and allocating samples based on the network’s convergence state.This strategy ensured improved detection accuracy while maintaining detection speed.Experimental results demonstrate that the proposed method achieves an average detection accuracy of 93.21%on the SeaShips dataset and 92.34%on the MCShips dataset.When compared to traditional YOLOXs,the method exhibits an improvement of 1.01%and 1.09%,respectively.The experimental results confirm that the proposed method,utilizing scale-adaptive receptive fields,can achieve high-precision detection of multi-scale ship targets.