基于Co-PSPNet的轻量级水下鱼体图像分割算法

A Lightweight Underwater Fish Body Image Segmentation Algorithm based on Co-PSPNet

摘要:

为解决水下环境中图像模糊和资源有限等问题,提出了一种基于Co-PSPNet网络的轻量级水下鱼体图像分割算法；主干网络使用了MobileNetV2,以降低网络的计算复杂度和参数量；引入了CoordConv模块,以增强网络对鱼体边界等细节信息的感知能力；将全局池化后的特征作为注意力机制网络的输入,以增强具有较高语义信息的特征；经过大量的实验评估,该算法在公开的水下鱼体图像数据集上取得了优越的性能；实际应用中,该算法满足了水下生态研究和水下机器人领域对水下鱼体图像分割的工程需求；通过对水下环境下图像模糊和资源限制等问题的解决,该算法为水下生态研究和水下机器人领域的应用提供了有效的图像分割解决方案。

Abstract:

To address issues such as image blurring and limited resources in underwater environments, a lightweight underwater fish segmentation algorithm based on the Co-PSPNet network is proposed. The main network employs MobileNetV2 to reduce computational complexity and parameter count.The CoordConv module is introduced to enhance the network"s perception of detailed information like fish boundaries.Features obtained after global pooling are used as inputs for an attention mechanism network, enhancing features with higher semantic information. Through extensive experimental evaluation, the algorithm demonstrates superior performance on publicly available underwater fish image datasets. In practical applications, this algorithm meets the engineering requirements for underwater fish image segmentation in the domains of underwater ecology research and underwater robotics. By addressing issues of image blurring and resource limitations in underwater environments, this algorithm provides an effective solution for image segmentation in underwater ecology research and underwater robotics applications.