Underwater object detection has higher requirements of running speed and deployment efficiency for the detector due to its specific environmental challenges. NMS of two- or one-stage object detectors and transformer architecture of query-based end-to-end object detectors are not conducive to deployment on underwater embedded devices with limited processing power. As for the detrimental effect of underwater color cast noise, recent underwater object detectors make network architecture or training complex, which also hinders their application and deployment on unmanned underwater vehicles. In this paper, we propose the Underwater DECO with improved deNoising training (U-DECN), the query-based end-to-end object detector (with ConvNet encoder-decoder architecture) for underwater color cast noise that addresses the above problems. We integrate advanced technologies from DETR variants into DECO and design optimization methods specifically for the ConvNet architecture, including Deformable Convolution in SIM and Separate Contrastive DeNoising Forward methods. To address the underwater color cast noise issue, we propose an Underwater Color DeNoising Query method to improve the generalization of the model for the biased object feature information by different color cast noise. Our U-DECN, with ResNet-50 backbone, achieves the best 64.0 AP on DUO and the best 58.1 AP on RUOD, and 21 FPS (5 times faster than Deformable DETR and DINO 4 FPS) on NVIDIA AGX Orin by TensorRT FP16, outperforming the other state-of-the-art query-based end-to-end object detectors. The code is available at https://github.com/LEFTeyex/U-DECN.

翻译：水下目标检测因其特定的环境挑战，对检测器的运行速度和部署效率提出了更高要求。两阶段或单阶段目标检测器的非极大值抑制（NMS）以及基于查询的端到端目标检测器的Transformer架构，均不利于在处理能力有限的水下嵌入式设备上部署。针对水下色彩偏移噪声的不利影响，近期的水下目标检测器使网络架构或训练过程变得复杂，这也阻碍了其在无人水下航行器上的应用与部署。本文提出具有改进去噪训练的水下DECO（U-DECN），这是一种针对水下色彩偏移噪声的基于查询的端到端目标检测器（采用ConvNet编码器-解码器架构），旨在解决上述问题。我们将DETR变体中的先进技术集成到DECO中，并专门为ConvNet架构设计了优化方法，包括SIM中的可变形卷积和分离对比去噪前向方法。为应对水下色彩偏移噪声问题，我们提出了一种水下色彩去噪查询方法，通过处理不同色彩偏移噪声导致的偏差目标特征信息，提升模型的泛化能力。我们的U-DECN以ResNet-50为骨干网络，在DUO数据集上取得了最佳的64.0 AP，在RUOD数据集上取得了最佳的58.1 AP，并在NVIDIA AGX Orin平台上通过TensorRT FP16实现了21 FPS（比可变形DETR和DINO的4 FPS快5倍），性能优于其他最先进的基于查询的端到端目标检测器。代码可在https://github.com/LEFTeyex/U-DECN获取。