Volume 40, Number 2, April 2022
|Page(s)||433 - 441|
|Published online||03 June 2022|
Underwater object detection algorithm based on channel attention and feature fusion
School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection. In order to solve the above-mentioned problems, an underwater object detection algorithm is proposed based on the channel attention and feature fusion for underwater optical images. The excitation residual module is designed based on the channel attention, and the forward propagation feature information is adaptively allocated weights to highlight the salience of different channel feature maps, which improves the network ability to extract high-frequency information from the underwater images. The multi-scale feature fusion module is designed to add a large scale feature map for object detection, which improves the detection performance of the network for small size objects by using its corresponding small size receptive field, and further improves the detection accuracy of the network for different size objects in the underwater environment. To improve the generalization performance of the network to the underwater environment, the data augmentation method based on the stitching and fusion is designed to simulate the overlap, occlusion and blurring of underwater objects, which improves the adaptability of the network to the underwater environment. Through experiments on the public dataset URPC, the algorithm in this paper improves the mean average precision by 5.42%, 3.20% and 0.9% compared with YOLOv3, YOLOv4 and YOLOv5, respectively, effectively improving the missed and false detection of objects of different sizes in complex underwater environments.
水下光学图像存在色偏、低对比度、目标模糊的现象，导致水下目标检测时存在漏检、误检等问题。针对上述问题，提出了一种基于通道注意力与特征融合的水下目标检测算法。基于通道注意力设计了激励残差模块，将前向传播的特征信息进行自适应分配权重，以突出不同通道特征图的显著性，提高了网络对水下图像高频信息的提取能力; 设计了多尺度特征融合模块，增加了大尺度特征图用于目标检测，利用其对应的小尺度感受野提高了网络对小尺寸目标的检测性能，进一步提高了网络对水下不同尺寸目标的检测精度; 为提高网络对水下环境的泛化性能，设计了基于拼接和融合的数据增强方法，模拟水下目标的重叠、遮挡和模糊情况，增强了网络对水下环境的适应性。通过在公共数据集URPC上的实验，与YOLOv3、YOLOv4和YOLOv5相比，所提算法的平均精度均值分别提升5.42%，3.20%和0.9%，有效改善了水下复杂环境中不同尺寸目标漏检、误检的问题。
Key words: underwater image / object detection / channel attention / multi-scale feature fusion / deep learning
关键字 : 水下图像 / 目标检测 / 通道注意力 / 多尺度特征融合 / 深度学习
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