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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.