YOLO：实时目标检测的新方法
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收藏 4.97MB PDF 举报 "YOLO(You Only Look Once)是一种实时目标检测算法，由Joseph Redmon等人在2016年提出。该算法通过将目标检测问题重新定义为回归问题，以解决传统方法中分类器用于检测的局限性。YOLO的核心在于使用一个单一的神经网络模型，直接从全图像中预测出边界框及其相关的类别概率，从而实现检测性能的优化。
在YOLO中，整个检测流程被整合到一个网络中，这允许对整个系统进行端到端的训练，优化目标是直接提升检测效果。这种统一的架构不仅提高了效率，而且在保持准确性的同时，实现了快速的图像处理速度。
YOLO基础模型可以在45帧每秒的速度下处理图像，而更小版本的Fast YOLO则以惊人的155帧每秒的速度运行，其精度甚至超过了其他实时检测器的两倍。尽管与其他最先进的检测系统相比，YOLO在定位错误上可能更多，但其误报（假阳性）的情况相对较少，这意味着它在避免误识别对象方面表现得更好。
YOLO算法的关键创新点包括：
1. **回归问题的定义**：不同于传统的基于分类的方法，YOLO将目标检测视为预测图像中每个像素点对应边界框的概率和类别概率的回归问题。
2. **单个神经网络**：整个检测过程在一个神经网络内完成，包括特征提取、边界框预测和类别概率计算。
3. **实时性能**：YOLO的高效设计使得它能够实现实时目标检测，尤其在Fast YOLO版本中，速度显著提升。
4. **端到端训练**：由于所有组件都在同一个网络中，YOLO可以利用反向传播直接优化检测性能，无需额外的后处理步骤。
5. **平衡精度与速度**：尽管在定位精确度上可能存在一些不足，但YOLO在减少误报方面表现出色，提供了速度与准确性的良好平衡。
YOLO的出现，为实时目标检测领域开辟了新的道路，后续的版本如YOLOv2和YOLOv3等继续优化了模型性能，引入了锚框（anchor boxes）、多尺度检测和空间金字塔池化等技术，进一步提升了检测精度和速度。这些改进使得YOLO成为了计算机视觉领域广泛应用的目标检测算法之一。"
You Only Look Once:
Unified, Real-Time Object Detection
Joseph Redmon
∗
, Santosh Divvala
∗†
, Ross Girshick
¶
, Ali Farhadi
∗†
University of Washington
∗
, Allen Institute for AI
†
, Facebook AI Research
¶
http://pjreddie.com/yolo/
Abstract
We present YOLO, a new approach to object detection.
Prior work on object detection repurposes classifiers to per-
form detection. Instead, we frame object detection as a re-
gression problem to spatially separated bounding boxes and
associated class probabilities. A single neural network pre-
dicts bounding boxes and class probabilities directly from
full images in one evaluation. Since the whole detection
pipeline is a single network, it can be optimized end-to-end
directly on detection performance.
Our unified architecture is extremely fast. Our base
YOLO model processes images in real-time at 45 frames
per second. A smaller version of the network, Fast YOLO,
processes an astounding 155 frames per second while
still achieving double the mAP of other real-time detec-
tors. Compared to state-of-the-art detection systems, YOLO
makes more localization errors but is less likely to predict
false positives on background. Finally, YOLO learns very
general representations of objects. It outperforms other de-
tection methods, including DPM and R-CNN, when gener-
alizing from natural images to other domains like artwork.
1. Introduction
Humans glance at an image and instantly know what ob-
jects are in the image, where they are, and how they inter-
act. The human visual system is fast and accurate, allow-
ing us to perform complex tasks like driving with little con-
scious thought. Fast, accurate algorithms for object detec-
tion would allow computers to drive cars without special-
ized sensors, enable assistive devices to convey real-time
scene information to human users, and unlock the potential
for general purpose, responsive robotic systems.
Current detection systems repurpose classifiers to per-
form detection. To detect an object, these systems take a
classifier for that object and evaluate it at various locations
and scales in a test image. Systems like deformable parts
models (DPM) use a sliding window approach where the
classifier is run at evenly spaced locations over the entire
image [10].
More recent approaches like R-CNN use region proposal
1. Resize image.
2. Run convolutional network.
3. Non-max suppression.
Dog: 0.30
Person: 0.64
Horse: 0.28
Figure 1: The YOLO Detection System. Processing images
with YOLO is simple and straightforward. Our system (1) resizes
the input image to 448 × 448, (2) runs a single convolutional net-
work on the image, and (3) thresholds the resulting detections by
the model’s confidence.
methods to first generate potential bounding boxes in an im-
age and then run a classifier on these proposed boxes. After
classification, post-processing is used to refine the bound-
ing boxes, eliminate duplicate detections, and rescore the
boxes based on other objects in the scene [13]. These com-
plex pipelines are slow and hard to optimize because each
individual component must be trained separately.
We reframe object detection as a single regression prob-
lem, straight from image pixels to bounding box coordi-
nates and class probabilities. Using our system, you only
look once (YOLO) at an image to predict what objects are
present and where they are.
YOLO is refreshingly simple: see Figure 1. A sin-
gle convolutional network simultaneously predicts multi-
ple bounding boxes and class probabilities for those boxes.
YOLO trains on full images and directly optimizes detec-
tion performance. This unified model has several benefits
over traditional methods of object detection.
First, YOLO is extremely fast. Since we frame detection
as a regression problem we don’t need a complex pipeline.
We simply run our neural network on a new image at test
time to predict detections. Our base network runs at 45
frames per second with no batch processing on a Titan X
GPU and a fast version runs at more than 150 fps. This
means we can process streaming video in real-time with
less than 25 milliseconds of latency. Furthermore, YOLO
achieves more than twice the mean average precision of
other real-time systems. For a demo of our system running
in real-time on a webcam please see our project webpage:
http://pjreddie.com/yolo/.
Second, YOLO reasons globally about the image when
1
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