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.

翻译：以锚为主的探测器通过建立地貌金字塔和直接在不同层次上为每个单元格设置不同的锚,来处理规模变化问题;然而,由于在每层中没有箱式锚和像素级特征之间的一对一对应,因此盒式锚很难指导对每个层中不同尺度特征的适应性学习;为了缓解问题,我们在本文件中提议在像素一级为每一层中的规模定制对象特征学习,在像素级中设置一个规模定制的弱分解块(SCWS)。通过将SCWS区块纳入单发探测器,将一个有尺寸的天体探测器(SCOD)建成一个大小不同的天体,以自然和准确地探测不同天体。此外,标准位置损失忽略了一个事实,即硬易的样品可能严重失衡。一个即将出现的问题是,由于不平衡,无法更准确地获得更精确的捆绑框。为了解决这一问题,在SCOD的大规模实验中具体规定了通过简单而有效的挤作业使IOU(AIU)适应性损失。