An Effective Strategy of Object Instance Segmentation in Sonar Images

Abstract

Instance segmentation is a task that involves pixel-level classification and segmentation of each object instance in images. Various CNN-based methods have achieved promising results in natural image instance segmentation. However, the noise interference, low resolution, and blurred edges bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose the Effective Strategy for Sonar Images Instance Segmentation (ESSIIS). We introduce ASception, a new network combining Atrous Spatial Pyramid Pooling (ASPP) and Extreme Inception (Xception). By integrating this with ResNet and transforming traditional convolutions into deformable convolutions, we further improve the ability of the network to extract features from sonar images. Additionally, we incorporate a bidirectional feature fusion module to enhance information fusion. Finally, we evaluate the detection accuracy and segmentation accuracy of the proposed method on the public sonar image dataset and the self-constructed dataset. ESSIIS attains a detection accuracy of 0.981 and a segmentation accuracy of 0.951 on SCTD, further impressively achieving 0.986 in both metrics when appraised on our dataset. The evaluation results demonstrate that the proposed method is more accurate, robust, and considerable for sonar image detection and segmentation.