There are still two problems in SDD causing some inaccurate results: (1) In
the process of feature extraction, with the layer-by-layer acquisition of
semantic information, local information is gradually lost, resulting into less
representative feature maps; (2) During the Non-Maximum Suppression (NMS)
algorithm due to inconsistency in classification and regression tasks, the
classification confidence and predicted detection position cannot accurately
indicate the position of the prediction boxes. Methods: In order to address
these aforementioned issues, we propose a new architecture, a modified version
of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector
(PSSD). Firstly, we improve the features by adding extra layers to SSD.
Secondly, we construct a simple and effective feature enhancement module to
expand the receptive field step by step for each layer and enhance its local
and semantic information. Finally, we design a more efficient loss function to
predict the IOU between the prediction boxes and ground truth boxes, and the
threshold IOU guides classification training and attenuates the scores, which
are used by the NMS algorithm. Main Results: Benefiting from the above
optimization, the proposed model PSSD achieves exciting performance in
real-time. Specifically, with the hardware of Titan Xp and the input size of
320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28
mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object
detection models. Besides, the proposed model performs significantly well with
larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS
COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove
that the proposed model has a better trade-off between speed and accuracy.

本研究提出了一种改进的单阶段物体检测器 (Precise Single Stage Detector, PSSD)，通过添加额外层、构建高效特征增强模块和设计更有效的损失函数优化了 SSD，提高了其对于局部和语义信息的分析能力，解决了特征抽取和非极大值抑制两个关键问题，进而在行为检测中取得了优异的表现。

精密单级检测器

Precise Single-stage Detector

We'd like to share a simple tweak of Single Shot Multibox Detector (SSD)
family of detectors, which is effective in reducing model size while
maintaining the same quality. We share box predictors across all scales, and
replace convolution between scales with max pooling. This has two advantages
over vanilla SSD: (1) it avoids score miscalibration across scales; (2) the
shared predictor sees the training data over all scales. Since we reduce the
number of predictors to one, and trim all convolutions between them, model size
is significantly smaller. We empirically show that these changes do not hurt
model quality compared to vanilla SSD.

通过共享盒子预测器并采用最大池化替代卷积之间的过程，成功降低了 Single Shot Multibox Detector 家族探测器的模型大小，同时保持相同的质量，避免了评分跨尺度的失真。

池化金字塔网络用于目标检测

Pooling Pyramid Network for Object Detection

We introduce a new large-scale dataset for the advancement of object
detection techniques and overhead object detection research. This satellite
imagery dataset enables research progress pertaining to four key computer
vision frontiers. We utilize a novel process for geospatial category detection
and bounding box annotation with three stages of quality control. Our data is
collected from WorldView-3 satellites at 0.3m ground sample distance, providing
higher resolution imagery than most public satellite imagery datasets. We
compare xView to other object detection datasets in both natural and overhead
imagery domains and then provide a baseline analysis using the Single Shot
MultiBox Detector. xView is one of the largest and most diverse publicly
available object-detection datasets to date, with over 1 million objects across
60 classes in over 1,400 km^2 of imagery.

该论文介绍了一个新的大规模数据集，该数据集可用于促进目标检测技术和高空目标检测研究。该数据集使用了一个新颖的地理空间类别检测和边界框注释过程，包括三个质量控制阶段。xView 是迄今为止最大、最多样化的公开目标检测数据集之一。