Spatial Orientation Relation Recognition for Water Surface Targets

Recently, extensive research efforts have concentrated on comprehending the semantic features of images in the field of computer vision. In order to address the spatial orientation relations among water surface targets (WSTs) in an image, which is a fundamental semantic feature, this paper focused on the recognition of spatial orientation relations. We first developed the water surface target spatial orientation vector field (WST-SOVF) algorithm, a novel end-to-end methodology, to recognize these spatial orientation relations among WSTs in an image. The WST-SOVF algorithm encodes the spatial orientation relation into the learning framework of a new deep convolutional neural network model, which comprises two distinct branches: the T-branch and the S-branch, both designed for the spatial feature extraction. The T-branch employs keypoint estimation to identify central points and classify the WST categories, while the S-branch constructs a spatial orientation vector field between WSTs, where each pixel in the field encodes the spatial orientation angle between two separated WSTs and collectively determines the category of spatial orientation. A fusion module was also designed to integrate the spatial feature obtained from both branches, thereby generating a comprehensive triple list that provides not only all the WSTs and their spatial orientation relations, but also their associated confidence levels. We performed a comparative evaluation of our WST-SOVF algorithm based on Huawei’s “Typical Surface/Underwater Target Recognition” dataset and the results demonstrated the outstanding performance of WST-SOVF algorithm.