Underwater Small Target Detection Based on Deformable Convolutional Pyramid
Shuhan Qi, Jianjun Du, Mingyan Wu, Hong Yi, Linlin Tang, Qian Tao, Xuan Wang
Abstract
Due to the problem of severe deformation, occlusion, diversified scenarios, general object detection methods cannot achieve satisfactory results in underwater object detection tasks. In this paper, we propose a two-stage Underwater Small Target Detection (USTD) network. In the proposed USTD, the Deformable Convolutional Pyramid(DCP) is proposed to deal with the problems of deformation, occlusion, and various object sizes effectively. Besides, we also propose a strategy of domain generalization based on curriculum learning to improve generalization in multi-domain environments, which is named as Phased Learning. Afterward, we construct an underwater target detection set (UTDS) to evaluate the accuracy of our method in underwater target detection tasks. Our method shows superior detection performance in experiments and reaches state-of-the-art for underwater target detection. Finally, in the 2020 China Underwater Robot Professional Contest (URPC), our method reached third place in terms of accuracy.