Current state-of-the-art convolutional architectures for object detection are
manually designed. Here we aim to learn a better architecture of feature
pyramid network for object detection. We adopt Neural Architecture Search and
discover a new feature pyramid architecture in a novel scalable search space
covering all cross-scale connections. The discovered architecture, named
NAS-FPN, consists of a combination of top-down and bottom-up connections to
fuse features across scales. NAS-FPN, combined with various backbone models in
the RetinaNet framework, achieves better accuracy and latency tradeoff compared
to state-of-the-art object detection models. NAS-FPN improves mobile detection
accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in
[32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy
with less computation time.

本文介绍了使用神经架构搜索方法学习目标检测特征金字塔网络的新架构 NAS-FPN。NAS-FPN 采用自下而上和自上而下的连接方式，通过结合 RetinaNet 框架中的各种骨干模型，相比当前最先进的目标检测模型，在精度和延迟的交易方面取得更好的效果。NAS-FPN 在移动检测精度方面比 [32] 中的最先进的 SSDLite with MobileNetV2 模型提高 2 个方差值，并以更少的计算时间实现了 48.3 的检测精度，超过了 Mask R-CNN [10] 的检测精度。

NAS-FPN：学习可扩展的特征金字塔架构用于目标检测

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object  Detection

Scene text detection methods based on deep learning have achieved remarkable
results over the past years. However, due to the high diversity and complexity
of natural scenes, previous state-of-the-art text detection methods may still
produce a considerable amount of false positives, when applied to images
captured in real-world environments. To tackle this issue, mainly inspired by
Mask R-CNN, we propose in this paper an effective model for scene text
detection, which is based on Feature Pyramid Network (FPN) and instance
segmentation. We propose a supervised pyramid context network (SPCNET) to
precisely locate text regions while suppressing false positives. Benefited from
the guidance of semantic information and sharing FPN, SPCNET obtains
significantly enhanced performance while introducing marginal extra
computation. Experiments on standard datasets demonstrate that our SPCNET
clearly outperforms start-of-the-art methods. Specifically, it achieves an
F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and
82.9% on Total-Text.

本文提出了一种基于 Feature Pyramid Network 和实例分割的模型，准确定位文本区域的同时抑制了虚警，实验结果表明该模型在场景文本检测方面表现优于现有方法。

使用监督金字塔上下文网络进行场景文本检测

Scene Text Detection with Supervised Pyramid Context Network

Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.

本文提出了一种新方法，即一个新的联合图像级联和特征金字塔网络，用于在遥感图像中提取多尺度的强和弱语义特征，并进行定向边界框的检测和旋转非极大值抑制，提高了模型的性能。同时，该模型还具有对图像和对象尺度和方向的鲁棒性，可在多类物体检测应用程序中部署。