论文标题
DFAM-DRET：基于可变形的注意机制在细长对象检测上发出
DFAM-DETR: Deformable feature based attention mechanism DETR on slender object detection
论文作者
论文摘要
对象检测是计算机视觉最重要的方面之一，它在各种域中取得了可观的结果。值得注意的是，很少有专注于细长对象检测的研究。 CNN被广泛用于对象检测中，但是由于固定的几何结构和采样点，它在细长的对象检测方面的性能很差。相比之下，可变形的DETR具有获取全局到特定特征的能力。即使它在细长对象检测准确性和效率中的表现都优于CNN，但结果仍然不满意。因此，我们提出了基于可变形的注意机制（DFAM），以提高纤细的对象检测准确性和可变形detr的效率。 DFAM具有可变形卷积和注意力机制的自适应采样点，这些卷积和注意力机制从骨干网络中的整个输入序列汇总了信息。该改进的检测器被称为基于特征的注意机制DITR（DFAM-DETR）。结果表明，DFAM-DRET在细长对象上实现了出色的检测性能。
Object detection is one of the most significant aspects of computer vision, and it has achieved substantial results in a variety of domains. It is worth noting that there are few studies focusing on slender object detection. CNNs are widely employed in object detection, however it performs poorly on slender object detection due to the fixed geometric structure and sampling points. In comparison, Deformable DETR has the ability to obtain global to specific features. Even though it outperforms the CNNs in slender objects detection accuracy and efficiency, the results are still not satisfactory. Therefore, we propose Deformable Feature based Attention Mechanism (DFAM) to increase the slender object detection accuracy and efficiency of Deformable DETR. The DFAM has adaptive sampling points of deformable convolution and attention mechanism that aggregate information from the entire input sequence in the backbone network. This improved detector is named as Deformable Feature based Attention Mechanism DETR (DFAM- DETR). Results indicate that DFAM-DETR achieves outstanding detection performance on slender objects.
