Real time object detection for autonomous AUVs using an attention–based fast–RCNN framework

This article considers the problem of fish monitoring in an underwater environment, where many problems might occur, including occlusion, pose changes, and complexity of the scene. Recognizing fish behavior is very important to develop various types of technologies able to provide more precise estimations and monitoring of fish populations in a long term. In this paper, we propose a novel method for underwater fish monitoring (shape modeling and pose estimation). Two main aspects of underwater image processing will be studied: classification and localisation. Additionally, we extract key point features from fish patterns. The fish position and motion are not sufficient features to avoid scene problems. Skeleton extraction could offer us a large range of additional information. It models an object as a set of points of a certain manifold. The 3-dimensional fish pose, along the track of its 3D motion, could depend on curve segments of the underlying manifold. Faster reccurent conventional neural networks (faster R-CNNs) will be used to extract the fish skeleton in different poses. Also, a 3-dimensional trajectory of multiple fish will be derived using a Kalman filter based on the previous feature matching process. The simulation is made for live fish in a fish tank. Experimental results show that our method outperforms relevant models in terms of precision, achieving a minimal accuracy of 94.2%.