CNN-based object detection methods have achieved significant progress in
recent years. The classic structures of CNNs produce pyramid-like feature maps
due to the pooling or other re-scale operations. The feature maps in different
levels of the feature pyramid are used to detect objects with different scales.
For more accurate object detection, the highest-level feature, which has the
lowest resolution and contains the strongest semantics, is up-scaled and
connected with the lower-level features to enhance the semantics in the
lower-level features. However, the classic mode of feature connection combines
the feature of lower-level with all the features above it, which may result in
semantics degradation. In this paper, we propose a skipped connection to obtain
stronger semantics at each level of the feature pyramid. In our method, the
lower-level feature only connects with the feature at the highest level, making
it more reasonable that each level is responsible for detecting objects with
fixed scales. In addition, we simplify the generation of anchor for bounding
box regression, which can further improve the accuracy of object detection. The
experiments on the MS COCO and Wider Face demonstrate that our method
outperforms the state-of-the-art methods.

这篇论文提出了一种跳跃连接的方法，以获得特征金字塔每个层级中更强的语义，并简化了边界框回归的锚点生成方式，从而进一步提高物体检测的准确性。

跳跃特征金字塔网络与网格锚点用于物体检测

Skipped Feature Pyramid Network with Grid Anchor for Object Detection

Applying deep learning to object detection provides the capability to
accurately detect and classify complex objects in the real world. However,
currently, few mobile applications use deep learning because such technology is
computation-intensive and energy-consuming. This paper, to the best of our
knowledge, presents the first detailed experimental study of a mobile augmented
reality (AR) client's energy consumption and the detection latency of executing
Convolutional Neural Networks (CNN) based object detection, either locally on
the smartphone or remotely on an edge server. In order to accurately measure
the energy consumption on the smartphone and obtain the breakdown of energy
consumed by each phase of the object detection processing pipeline, we propose
a new measurement strategy. Our detailed measurements refine the energy
analysis of mobile AR clients and reveal several interesting perspectives
regarding the energy consumption of executing CNN-based object detection.
Furthermore, several insights and research opportunities are proposed based on
our experimental results. These findings from our experimental study will guide
the design of energy-efficient processing pipeline of CNN-based object
detection.

本文第一次详细研究了在移动增强现实客户端中，执行基于卷积神经网络的物体检测的能量消耗和检测延迟，提出了新的测量策略，从而发现了执行基于 CNN 的物体检测的能量消耗问题。结果表明，我们的实验结果为设计能有效处理基于 CNN 的物体检测的能量有效型处理流程提供了指导。