Abstract
Underwater object detection using sonar imagery has become a critical and rapidly evolving research domain within marine technology. However, sonar images are characterized by lower resolution and sparser features compared to optical images, which seriously degrades the performance of object this http URL address these challenges, we specifically propose a Detection Transformer (DETR) architecture optimized with a Neural Architecture Search (NAS) approach called NAS-DETR for object detection in sonar images. First, an improved Zero-shot Neural Architecture Search (NAS) method based on the maximum entropy principle is proposed to identify a real-time, high-representational-capacity CNN-Transformer backbone for sonar image detection. This method enables the efficient discovery of high-performance network architectures with low computational and time overhead. Subsequently, the backbone is combined with a Feature Pyramid Network (FPN) and a deformable attention-based Transformer decoder to construct a complete network architecture. This architecture integrates various advanced components and training schemes to enhance overall performance. Extensive experiments demonstrate that this architecture achieves state-of-the-art performance on two Representative datasets, while maintaining minimal overhead in real-time efficiency and computational complexity. Furthermore, correlation analysis between the key parameters and differential entropy-based fitness function is performed to enhance the interpretability of the proposed framework. To the best of our knowledge, this is the first work in the field of sonar object detection to integrate the DETR architecture with a NAS search mechanism.
Abstract (translated)
使用声呐图像进行水下物体检测已成为海洋技术领域中一个关键且迅速发展的研究方向。然而，与光学图像相比，声呐图像的分辨率较低、特征较稀疏，这严重影响了目标识别性能。为了解决这些挑战，我们特别提出了一种基于神经架构搜索（NAS）优化的方法——称为NAS-DETR的目标检测框架，用于声呐图像中的物体检测。首先，提出了一种改进的零样本神经架构搜索（NAS）方法，该方法基于最大熵原则，旨在识别适用于声呐图像检测的实时、高表示能力的CNN-Transformer骨干网络。这种方法能够有效地发现高性能网络结构，并且具有较低的计算和时间开销。 随后，将此骨干网络与特征金字塔网络（FPN）以及基于可变形注意力机制的Transformer解码器相结合，构建了一个完整的网络架构。该架构集成了各种先进组件和训练方案，以提升整体性能。广泛的实验表明，在两个代表性数据集上，该架构实现了最先进的性能，并且在实时效率和计算复杂度方面保持了极低的开销。 此外，还进行了关键参数与基于差分熵的适应性函数之间的相关性分析，以增强所提出框架的可解释性。据我们所知，这是首次将DETR架构与NAS搜索机制结合用于声呐物体检测领域的研究工作。
URL
https://arxiv.org/abs/2505.06694
