Development of an Efficient Algorithm for Sea Surface Enteromorpha Object Detection

In recent years, frequent outbreaks of Enteromorpha disasters in the Yellow Sea have caused substantial economic losses to coastal cities. In order to tackle the challenges of the low detection accuracy and high false negative rate of Enteromorpha detection in complex marine environments, this study proposes an object detection algorithm CEE-YOLOv8, improved from YOLOv8n, and establishes the Enteromorpha dataset. Firstly, this study integrates a C2f-ConvNeXtv2 module into the YOLOv8n Backbone network to augment multi-scale feature extraction capabilities. Secondly, an ECA attention mechanism is incorporated into the Neck network to enhance the perception ability of the model to different sizes of Enteromorpha. Finally, the CIoU loss function is replaced with EIoU to optimize bounding box localization precision. Experiment results on the self-made Enteromorpha dataset show that the improved CEE-YOLOv8 model achieves a 3.2% increase in precision, a 3.3% improvement in recall, and a 4.1% gain in mAP50-95 compared to the benchmark model YOLOv8n. Consequently, the proposed model provides robust technical support for future Enteromorpha monitoring initiatives.