COTS recognition and detection based on Improved YOLO v5 model

At present, the ecological environment of the Great Barrier Reef is becoming more and more fragile. Stopping the propagation and spread of COTS is an important part of protecting the environment of the Great Barrier Reef. It is becoming more and more important to identify and detect the distribution of COTS. With the development of computer science, deep learning technology has been widely used in the field of image recognition. Based on YOLOv5 algorithm and WBF model, this paper constructs a more accurate and efficient detection model to frame the distribution position of COTS. Our algorithm has been verified on the KAGGLE platform. The results show that our algorithm has great advantages in detection performance compared with other detection models. Quantitatively, the F
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value of our model is 39.241% and 5.263% higher than that of Faster R-CNN and YOLO v5 algorithms, respectively, which provides a certain reference for the protection of the Great Barrier Reef.