Kavli Affiliate: Feng Wang
| First 5 Authors: Wen Feng, Wang Mei, Hu Xiaojie, ,
| Summary:
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.
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