Detection and segmentation of underwater objects from forward-looking sonar based on a modified Mask RCNN

Abstract

Nowadays, high-frequency forward-looking sonar is an effective device to obtain the main information of underwater objects. Detection and segmentation of underwater objects are also one of the key topics of current research. Deep learning has shown excellent performance in image features extracting and has been extensively used in image object detection and instance segmentation. With the network depth increasing, training accuracy gets saturated and training parameters also increase rapidly. In this paper, a series of residual blocks are used to build a 32-layer feature extraction network and take place of the Resnet50/101 in Mask RCNN, which reduces the training parameters of the network while guaranteeing the detection performance. The parameters of the proposed network are 29% less than Resnet50 and 50.2% less than Resnet101, which is of great significance for future hardware implementation. In addition, Adagrad optimizer is introduced into this research to improve the detection performance of sonar images. Finally, the object detection results of 500 test sonar images show that the mAP is 96.97% that is only 0.18% less than Resnet50 (97.15%) but more than Resnet101 (95.15%).