MACHINE LEARNING-BASED IMAGE DETECTION OF DEEP-SEA SEAMOUNTS CREATURES

Object detection algorithms, a popular research direction in computer vision, are valued for their wide range of applications. However, its applications in marine deep-sea biology are rarely seen. Three reasons are hidden among them: first, the lack of marine deep-sea datasets; second, the difficulty of network detection units to meet taxonomic requirements (both in speed and accuracy); and third, the difficulty of judging the network as a whole. To solve these problems to achieve high accuracy and high fineness for deep-sea target detection tasks, this paper proposes a creature detection model (CDM) based on YOLO V7 tiny network incorporating the idea of a bag of features. The network proposes a complementary LOSS function to optimize the model error caused by the polarization of underwater robots (ROVs) during deep-sea operations and uses an unsupervised clustering algorithm to generate a new dictionary for underwater species classification based on the detection.