Multi-scale feature fusion algorithm for underwater remote object detection in forward-looking sonar images

Abstract

With the increasing demand for underwater environment monitoring and remote object identification, underwater object detection algorithms based on forwardlooking sonar images have become a research hotspot. A kind of algorithm called remote deep feature fusion detection network (RDFFRN) is proposed, which aims to solve the problems of object shadow and background confusion and low detection accuracy of small objects in the underwater object detection algorithm of sonar images. First, the model proposes the deep feature extraction and fusion module (DFEFM). It achieves more multilayered feature fusion and captures more local details. Finally, the RDFFRN proposes a new head detection network, which increases the proportion of small-sized feature maps and improves the model's discrimination ability for small underwater objects. After extensive experiments on a sonar images dataset, the RDFFRN is verified to be superior to other object detection methods. The RDFFDN improves the mean average accuracy by 3.2% over the baseline model. It can be shown that RDFFRN has a broad application prospect in the underwater operation field.