论文标题
SCRDET ++：通过实例级别的特征来检测小，混乱和旋转的物体，并旋转损失平滑
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
论文作者
论文摘要
在现实世界中，小小的和混乱的物体很常见，这对于检测而言是挑战性的。当对象旋转时，难度会进一步发音，因为传统检测器通常会通常将对象定位在水平边界框中，从而使感兴趣的区域被背景或附近的交织对象污染。在本文中，我们首先是创新的，介绍了对象检测的思想。在特征映射上进行实例级降级，以增强对小且混乱的对象的检测。为了处理旋转变化，我们还为平滑的L1损失添加了一个新颖的IOU常数因子，以解决长期的边界问题，而对于我们的分析，这主要是由角（POA）的周期性和边缘的交换性（EOE）引起的。通过梳理这两个功能，我们提出的检测器称为SCRDET ++。在大型航空图像上进行广泛的实验公共数据集DOTA，DIOR，UCAS-AOD以及自然图像数据集可可，场景文本数据集ICDAR2015，小型交通灯数据集BSTLD和我们发布的S $^2 $^2 $ tld。结果表明我们方法的有效性。已发布的数据集S2TLD公开可用，其中包含5,786张图像，其中五个类别的交通灯实例为14,130个。
Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detectors often routinely locate the objects in horizontal bounding box such that the region of interest is contaminated with background or nearby interleaved objects. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects. To handle the rotation variation, we also add a novel IoU constant factor to the smooth L1 loss to address the long standing boundary problem, which to our analysis, is mainly caused by the periodicity of angular (PoA) and exchangeability of edges (EoE). By combing these two features, our proposed detector is termed as SCRDet++. Extensive experiments are performed on large aerial images public datasets DOTA, DIOR, UCAS-AOD as well as natural image dataset COCO, scene text dataset ICDAR2015, small traffic light dataset BSTLD and our released S$^2$TLD by this paper. The results show the effectiveness of our approach. The released dataset S2TLD is made public available, which contains 5,786 images with 14,130 traffic light instances across five categories.
