We present some updates to YOLO! We made a bunch of little design changes to
make it better. We also trained this new network that's pretty swell. It's a
little bigger than last time but more accurate. It's still fast though, don't
worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but
three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3
is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5
mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always,
all the code is online at this https URL

本研究介绍了 YOLOv3 目标检测算法的改进，包括设计修改、更精准的新网络以及在性能方面的提升。与其他算法相比，YOLOv3 具有更快的运行速度和相似的检测表现。

YOLOv3: 一项渐进式的改进

YOLOv3: An Incremental Improvement

Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector. The Convolutional Neural Networks
(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust
under different conditions (e.g., variations in camera, lighting, and
background). Specially for character segmentation and recognition, we design a
two-stage approach employing simple data augmentation tricks such as inverted
License Plates (LPs) and flipped characters. The resulting ALPR approach
achieved impressive results in two datasets. First, in the SSIG dataset,
composed of 2,000 frames from 101 vehicle videos, our system achieved a
recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better
than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,
respectively) and considerably outperforming previous results (81.80%). Second,
targeting a more realistic scenario, we introduce a larger public dataset,
called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos
and 4,500 frames captured when both camera and vehicles are moving and also
contains different types of vehicles (cars, motorcycles, buses and trucks). In
our proposed dataset, the trial versions of commercial systems achieved
recognition rates below 70%. On the other hand, our system performed better,
with recognition rate of 78.33% and 35 FPS.

本文提出了基于 YOLO 名物体检测器的自动车牌识别系统，使用卷积神经网络 (CNN) 对每个车牌识别阶段进行训练和微调，实现在不同条件下具有稳健性。针对字符分割和识别，本文设计了一个两阶段方法，并采用倒置车牌和翻转字符等简单的数据增强技巧。该系统在两个数据集中均取得了卓越的成绩。

基于 YOLO 检测器的鲁棒的实时自动车牌识别

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO  Detector

An adversarial example is an example that has been adjusted to produce a
wrong label when presented to a system at test time. To date, adversarial
example constructions have been demonstrated for classifiers, but not for
detectors. If adversarial examples that could fool a detector exist, they could
be used to (for example) maliciously create security hazards on roads populated
with smart vehicles. In this paper, we demonstrate a construction that
successfully fools two standard detectors, Faster RCNN and YOLO. The existence
of such examples is surprising, as attacking a classifier is very different
from attacking a detector, and that the structure of detectors - which must
search for their own bounding box, and which cannot estimate that box very
accurately - makes it quite likely that adversarial patterns are strongly
disrupted. We show that our construction produces adversarial examples that
generalize well across sequences digitally, even though large perturbations are
needed. We also show that our construction yields physical objects that are
adversarial.

该研究证明了构建对 Faster RCNN 和 YOLO 这两种检测器具有迷惑性的对抗性例子的存在，这意味着对检测器的攻击不仅仅局限于分类器，并且可能会潜在威胁到智能车辆行驶的道路安全。