Recognition and statistical analysis of coastal marine aquacultural cages based on R3Det single-stage detector: A case study of Fujian Province, China

Abstract

Fujian Province, located in the southeastern coast of China, has a highly developed coastal marine net cage aquaculture industry. The scale and density of marine aquaculture, especially net cages aquaculture, in China are increasing, which has seriously affected coastal and nearshore environments. Thus, methods for accurately obtaining the area and density of aquaculture net cages hold great significance. Because of the scattered distribution of net cage aquaculture areas, it is difficult to obtain an accurate net cage aquaculture area with traditional remote-sensing extraction methods. Therefore, this paper proposes a method for accurately identifying of the area of aquaculture net cages by combining R3Det single-stage detector and fishnet segmentation technology. A constructed sensing net cage dataset was used for model training and a fishing net segmentation technology was used to segment the remote-sensing image into R3Det testable image. The experimental results showed that the extraction accuracy of this method for round and square net cages was 92.65% and 98.06%, respectively, and the extraction accuracy of the actual net cage culture area was 97.24%. We concluded that the total area of net cage aquaculture in Fujian Province in 2019–2020 was 4363.58 ha, mainly in square net cages. The area of aquaculture was greater in the northern and southern parts of the province and less in the middle. The density of aquaculture was high in the south and low in the north. Compared with traditional remote-sensing extraction methods and neural networks, this solution reduced the impact of similar features on the classification target, realized the application of high-precision, large-area remote-sensing images on the R3Det single-stage detector, and improved the recognition accuracy and the robustness of the model.