Layer-wise Customized Weak Segmentation Block and AIoU Loss for Accurate
Object Detection
- URL: http://arxiv.org/abs/2108.11021v1
- Date: Wed, 25 Aug 2021 02:42:28 GMT
- Title: Layer-wise Customized Weak Segmentation Block and AIoU Loss for Accurate
Object Detection
- Authors: Keyang Wang, Lei Zhang, Wenli Song, Qinghai Lang, Lingyun Qin
- Abstract summary: We propose a scale-customized weak segmentation (SCWS) block at the pixel level for scale customized object feature learning in each layer.
By integrating the SCWS blocks into the single-shot detector, a scale-aware object detector (SCOD) is constructed to detect objects of different sizes naturally and accurately.
- Score: 7.3179451998143605
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The anchor-based detectors handle the problem of scale variation by building
the feature pyramid and directly setting different scales of anchors on each
cell in different layers. However, it is difficult for box-wise anchors to
guide the adaptive learning of scale-specific features in each layer because
there is no one-to-one correspondence between box-wise anchors and pixel-level
features. In order to alleviate the problem, in this paper, we propose a
scale-customized weak segmentation (SCWS) block at the pixel level for scale
customized object feature learning in each layer. By integrating the SCWS
blocks into the single-shot detector, a scale-aware object detector (SCOD) is
constructed to detect objects of different sizes naturally and accurately.
Furthermore, the standard location loss neglects the fact that the hard and
easy samples may be seriously imbalanced. A forthcoming problem is that it is
unable to get more accurate bounding boxes due to the imbalance. To address
this problem, an adaptive IoU (AIoU) loss via a simple yet effective squeeze
operation is specified in our SCOD. Extensive experiments on PASCAL VOC and MS
COCO demonstrate the superiority of our SCOD.
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