一种时空特征聚合的水下珊瑚礁鱼检测方法

水下监控视频中的珊瑚礁鱼检测面临着视频成像质量不高、水下环境复杂、珊瑚礁鱼视觉多样性高等困难，是一个极具挑战的视觉目标检测问题，如何提取高辨识度的特征成为制约检测精度提升的关键。提出了一种时空特征聚合的水下珊瑚礁鱼检测方法，通过设计视觉特征聚合和时序特征聚合2个模块，融合多个维度的特征以实现这一目标。前者设计了自顶向下的切分和自底向上的归并方案，可实现不同分辨率多层卷积特征图的有效聚合；后者给出了一种帧差引导的相邻帧特征图融合方案，可通过融合多帧特征图强化运动目标及其周边区域的特征表示。公开数据集上的实验表明:基于以上2个模块设计的时空特征聚合网络可以实现对水下珊瑚礁鱼的有效检测，相比于多个主流方法和模型取得了更高的检测精度。 

An underwater coral reef fish detection approach based on aggregation of spatio-temporal features

It is challenging to detect coral reef fish from underwater surveillance videos, due to issues like poor video imaging quality, complex underwater environment, high visual diversity of coral reef fish, etc. Extracting discriminative features to characterize the fishes has become a crucial issue that dominates the detection accuracy. This paper proposes an underwater coral reef fish detection method based on aggregation of spatio-temporal features. It is achieved by designing two modules for visual and temporal feature aggregation and fusing multi-dimensional features. The former designs a top-down partition and a bottom-up merging, which achieve effective aggregation of feature maps of different convolutional layers with varying resolutions. The latter devises a temporal feature fusion scheme based on the pixel difference between adjacent frames. It enhances the feature representation of moving objects and their surrounding area through the fusion of feature maps coming from adjacent frames. Experiments on a public dataset show that, by employing the spatio-temporal aggregation network built on top of the two proposed modules, we can effectively detect coral reef fishes in the challenging underwater environment. Higher detection accuracy are obtained compared with the existing methods and popular detection models.