  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.
