RESEARCH ON DETECTION OF SPARTINA ALTERNIFLORA BASED ON SA-YOLO

In view of the difficulty and high cost of monitoring the invasion of small aggregations of Spartina alterniflora in coastal wetlands, this study proposes a SA-YOLO detection model. First, by adopting a lightweight cascade attention mechanism as the feature extraction part of the network, the model's ability to extract features from Spartina alterniflora images is optimized. Secondly, the convolution layer with an improved adaptive attention mechanism is added to optimize feature extraction, dynamically adjust the weight of the feature map, and reduce the amount of calculation. Thirdly, the improved adaptive convolution network is used to optimize the original neck layer, improve the model's ability to integrate Spartina alterniflora image features, and reduce the amount of calculation. Finally, a Spartina alterniflora recognition system is independently built. The system effectively implements the proposed method and realizes the detection and recording of Spartina alterniflora information. This study successfully verifies the effectiveness of the proposed method by conducting experiments on the actual collected Spartina alterniflora dataset. The test results show that the recall rate and accuracy of the proposed SA-YOLO Spartina alterniflora detection model are 94.5% and 92.4%, respectively, both reaching a high level. It can be seen that the model can complete the identification and detection tasks of Spartina alterniflora, providing a solution for the identification and information collection of Spartina alterniflora in coastal areas.