基于自监督学习与可变形特征金字塔，提升水下多目标检测精度
Detection of Underwater Multi-Targets Based on Self-Supervised Learning and Deformable Path Aggregation Feature Pyramid Network
- 采用SimSiam自监督预训练，增强水下特征表示能力
- 引入可变形卷积与空洞卷积，扩大感受野，改善遮挡与密集分布问题
- 使用EIoU损失函数，分离计算框宽高误差，提升定位精度
为克服水下环境带来的挑战并提升目标检测模型的准确率与鲁棒性，本文构建了一个专门用于水下目标检测的数据集，并提出一种高效的水下多目标检测算法。基于SimSiam结构的自监督学习被用于水下目标检测网络的预训练，以增强特征表达。针对水下目标因低对比度、相互遮挡和密集分布导致的检测精度低的问题，通过引入可变形卷积与空洞卷积，构建了适用于水下目标检测的检测模型，有效扩大感受野，获取更丰富的信息。此外，引入回归损失函数EIoU，分别计算预测框的宽度与高度损失，从而提升模型性能。实验结果表明，所提检测器在水下目标检测任务中显著提升了准确率。
原文摘要 · Abstract (English)
To overcome the constraints of the underwater environment and improve the accuracy and robustness of underwater target detection models, this paper develops a specialized dataset for underwater target detection and proposes an efficient algorithm for underwater multi-target detection. A self-supervised learning based on the SimSiam structure is employed for the pre-training of underwater target detection network. To address the problems of low detection accuracy caused by low contrast, mutual occlusion and dense distribution of underwater targets in underwater object detection, a detection model suitable for underwater target detection is proposed by introducing deformable convolution and dilated convolution. The proposed detection model can obtain more effective information by increasing the receptive field. In addition, the regression loss function EIoU is introduced, which improves model performance by separately calculating the width and height losses of the predicted box. Experiment results show that the accuracy of the underwater target detection has been improved by the proposed detector.
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