Feature Cascade Underwater Object Detection Based on Stereo Segmentation

Abstract

Kong, W.; Yang, M., and Huang, Q., 2020. Feature cascade underwater object detection based on stereo segmentation. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 140–144. Coconut Creek (Florida), ISSN 0749-0208.Underwater object detection is a hot topic in deep learning in recent years. It is difficult to quickly complete the task of object detection due to the complex underwater environment. This article presents a novel neural network architecture, namely, Feature Cascade Underwater Object Detection Network. First, the estimated weights of different scales are obtained through the key region detection module. Then in the second module, the complete segmentation image is obtained through the multiple scale segmentation method. Third, cascade multilevel weights are estimated, along with stereo segmentation, for layer by layer integration. Finally, the predicted underwater object detection results. We test our network on the URPC2017 and LABELED-FISHES-IN-THE-WILD dataset. The average accuracy of our algorithm is higher than the other algorithms in single-category and multicategory underwater object detection tasks. Especially in the fish object detection task, our network obtains an average accuracy value that reaches 93.4%.