Yang, Y.; Liang, W.; Zhou, D.; Zhang, Y.; Xu, G. Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution. J. Mar. Sci. Eng.2023, 11, 2228.
Yang, Y.; Liang, W.; Zhou, D.; Zhang, Y.; Xu, G. Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution. J. Mar. Sci. Eng. 2023, 11, 2228.
Yang, Y.; Liang, W.; Zhou, D.; Zhang, Y.; Xu, G. Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution. J. Mar. Sci. Eng.2023, 11, 2228.
Yang, Y.; Liang, W.; Zhou, D.; Zhang, Y.; Xu, G. Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution. J. Mar. Sci. Eng. 2023, 11, 2228.
Abstract
Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed 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, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。
Copyright:
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