Enhanced Mask R-CNN Incorporating CBAM and Soft-NMS for Identification and Monitoring of Offshore Aquaculture Areas

The use of remote sensing images to analyze the change characteristics of large-scale aquaculture areas and monitor aquaculture violations is of great significance for exploring the law of marine aquaculture and assisting the monitoring and standardization of aquaculture areas. In this study, a violation monitoring framework for marine aquaculture areas based on image recognition using an enhanced Mask R-CNN architecture incorporating a convolutional block attention module (CBAM) and soft non-maximum suppression (Soft-NMS) is proposed and applied in Sandu'ao. The results show that the modified Mask R-CNN, when compared to the most basic Mask R-CNN model, exhibits higher accuracy in identifying marine aquaculture areas. The aquaculture patterns in the Xiapu region are characterized by two peak periods of aquaculture area fluctuations, occurring in March and October. Conversely, July marks the month with the smallest aquaculture area in the region and is influenced by factors such as water temperature and aquaculture cycle. Significant changes in the aquaculture area were observed in January, March, June, August, and October, necessitating rigorous monitoring. Furthermore, monitoring and analysis of aquaculture areas have revealed that despite the reduction in illegal aquaculture acreage since 2017 due to the implementation of functional zone planning for marine aquaculture areas, illegal aquaculture activities remain prevalent in prohibited and restricted zones in Xiapu, accounting for a considerable proportion.