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.
翻译:值得指出的是,很少有侧重于细微物体探测的研究。有线电视新闻网被广泛用于物体探测,但是由于固定的几何结构和取样点,在细微物体探测方面表现不佳。相比之下,变形的DETR有能力从全球获取具体特征。尽管它比有线电视新闻网在微粒物体探测准确性和效率方面表现优于有线电视新闻网,但结果仍然不尽人意。因此,我们提议基于变形的地物注意机制(DFAM)提高可变形的DETR的细微物体探测精确性和效率。DFAM具有可变形变形变形变形变形变形和注意机制的调整采样点,以汇总主干网整个输入序列的信息。这种改进的探测器被命名为基于注意机制的变形地物。结果显示,DFAM-DETR在细微物体上取得了杰出的探测性能。