Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution

Abstract

Cultural artifacts found underwater are located in complex environments with poor imaging conditions. In addition, the artifacts themselves present challenges for automated object detection owing to variations in their shape and texture caused by breakage, stacking, and burial. To solve these problems, this paper proposes an underwater cultural object detection algorithm based on the deformable deep aggregation network model for autonomous underwater vehicle (AUV) exploration. To fully extract the object feature information of underwater objects in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. In addition, the approach also incorporates a BAM module for feature optimization, which enhances the potential feature information of the object while weakening the background interference. Finally, the object prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validated and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, our method has been practically deployed on an AUV. In the field testing over a shipwreck site, the artifact detection frame rate reached up to 18 fps, which satisfies the real-time object detection requirement.

Keywords

• autonomous underwater vehicle
• cultural artifact object detection
• deformable convolution
• multi-scale deep aggregation
• attention mechanism