YOLOv6：工业应用的单阶段目标检测框架

需积分: 0 65 浏览量更新于2024-06-26 收藏 1.03MB PDF 举报

本文档探讨了名为"YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications"的论文，该研究旨在为工业应用提供一个高效的单阶段目标检测框架。YOLOv6是YOLO系列的最新版本，继承了YOLO（You Only Look Once）的传统，即一次前向传播就能完成物体检测，从而在速度和准确性上寻求平衡。论文的核心关注点在于工业场景下的性能优化，这包括对实时性和资源利用率的需求。 YOLOv6的设计目标是在保持较高检测精度的同时，减少模型复杂度和计算开销，使之适应于资源有限的工业设备。为了实现这一目标，作者可能采用了先进的架构改进，如更深层次的网络结构、特征融合技术、以及量化技术来减小模型大小，提高在如Tesla T4 GPU上的运行效率。在图1中，作者展示了YOLOv6与其他当前最先进的高效对象检测器（如YOLOv5、YOLOv7、YOLOX和PP-YOLOE）在延迟（ms）和吞吐量（FPS）方面的对比，其中在保持32批次输入时，YOLOv6展示出出色的性能，尤其是在较低的延迟下仍能保持较高的平均精度（COCO AP%）。论文可能深入分析了YOLOv6的具体组成部分，例如不同规模的模型（N、T、S、M、L），以及量化版YOLOv6-S的表现，这些模型大小各异，以适应不同的硬件配置和部署环境。此外，可能会讨论如何通过调整模型的深度和宽度、优化算法或采用混合精度（如FP16）来优化模型在工业级应用中的实际运行情况。为了满足工业环境的挑战，论文可能还涉及到了模型的部署策略、推理时的内存管理和能耗分析，这些都是衡量一个目标检测框架是否真正实用的关键因素。同时，由于标题中的“毕业设计”标签，可以推测这可能是某个学生的研究成果，因此论文也可能包含了方法论、实验设计、结果验证以及对未来研究方向的讨论。这篇论文深入研究了YOLOv6作为一款面向工业应用的单阶段目标检测框架，其在速度、准确性和适应性方面的优势，以及与同类技术的比较，对于那些关注工业级计算机视觉任务的开发者和研究人员具有重要的参考价值。

utilizes the computing power of the hardware, resulting in

a signiﬁcant decrease in inference latency while enhancing

the representation ability in the meantime.

However, we notice that with the model capacity further

expanded, the computation cost and the number of param-

eters in the single-path plain network grow exponentially.

To achieve a better trade-off between the computation bur-

den and accuracy, we revise a CSPStackRep Block to build

the backbone of medium and large networks. As shown

in Fig. 3 (c), CSPStackRep Block is composed of three 1×1

convolution layers and a stack of sub-blocks consisting of

two RepVGG blocks [3] or RepConv (at training or infer-

ence respectively) with a residual connection. Besides, a

cross stage partial (CSP) connection is adopted to boost

performance without excessive computation cost. Com-

pared with CSPRepResStage [45], it comes with a more

succinct outlook and considers the balance between accu-

racy and speed.

RepConv

𝟏×𝟏

Conv

RepConv

𝑁

𝟏×𝟏

Conv

𝟏×𝟏

Conv

(a)

(c)

: Element-wise add

: Concatenation over channel dimension

ReLU

𝟏×𝟏

Conv

𝟑×𝟑

Conv

ReLU

𝑁×

RepVGG block

𝑁×

(b)

RepConv

ReLU

Figure 3: (a) RepBlock is composed of a stack of RepVGG

blocks with ReLU activations at training. (b) During infer-

ence time, RepVGG block is converted to RepConv. (c)

CSPStackRep Block comprises three 1×1 convolutional

layers and a stack of sub-blocks of double RepConvs fol-

lowing the ReLU activations with a residual connection.

2.1.2 Neck

In practice, the feature integration at multiple scales has

been proved to be a critical and effective part of object de-

tection [9, 21, 24, 40]. We adopt the modiﬁed PAN topol-

ogy [24] from YOLOv4 [1] and YOLOv5 [10] as the base

of our detection neck. In addition, we replace the CSP-

Block used in YOLOv5 with RepBlock (for small models)

or CSPStackRep Block (for large models) and adjust the

width and depth accordingly. The neck of YOLOv6 is de-

noted as Rep-PAN.

2.1.3 Head

Efﬁcient decoupled head The detection head of

YOLOv5 is a coupled head with parameters shared be-

tween the classiﬁcation and localization branches, while its

counterparts in FCOS [41] and YOLOX [7] decouple the

two branches, and additional two 3×3 convolutional layers

are introduced in each branch to boost the performance.

In YOLOv6, we adopt a hybrid-channel strategy to build

a more efﬁcient decoupled head. Speciﬁcally, we reduce

the number of the middle 3×3 convolutional layers to only

one. The width of the head is jointly scaled by the width

multiplier for the backbone and the neck. These modiﬁca-

tions further reduce computation costs to achieve a lower

inference latency.

Anchor-free Anchor-free detectors stand out because of

their better generalization ability and simplicity in decod-

ing prediction results. The time cost of its post-processing

is substantially reduced. There are two types of anchor-

free detectors: anchor point-based [7, 41] and keypoint-

based [16, 46, 53]. In YOLOv6, we adopt the anchor point-

based paradigm, whose box regression branch actually pre-

dicts the distance from the anchor point to the four sides of

the bounding boxes.

2.2. Label Assignment

Label assignment is responsible for assigning labels to

predeﬁned anchors during the training stage. Previous work

has proposed various label assignment strategies ranging

from simple IoU-based strategy and inside ground-truth

method [41] to other more complex schemes [5, 7, 18, 48,

51].

SimOTA OTA [6] considers the label assignment in ob-

ject detection as an optimal transmission problem. It deﬁnes

positive/negative training samples for each ground-truth ob-

ject from a global perspective. SimOTA [7] is a simpli-

ﬁed version of OTA [6], which reduces additional hyper-

parameters and maintains the performance. SimOTA was

utilized as the label assignment method in the early version

of YOLOv6. However, in practice, we ﬁnd that introducing

SimOTA will slow down the training process. And it is not

rare to fall into unstable training. Therefore, we desire a

replacement for SimOTA.

Task alignment learning Task Alignment Learning

(TAL) was ﬁrst proposed in TOOD [5], in which a uniﬁed

metric of classiﬁcation score and predicted box quality is

designed. The IoU is replaced by this metric to assign object

labels. To a certain extent, the problem of the misalignment

of tasks (classiﬁcation and box regression) is alleviated.

The other main contribution of TOOD is about the task-

aligned head (T-head). T-head stacks convolutional layers to

build interactive features, on top of which the Task-Aligned

Predictor (TAP) is used. PP-YOLOE [45] improved T-

head by replacing the layer attention in T-head with the

剩余16页未读，继续阅读

福尔摩星儿

粉丝: 0
资源: 229

YOLOv6：工业应用的单阶段目标检测框架

基于YOLOv8-Pose的姿态识别项目，带数据集可直接跑通的源码

yolov论文-gradio-yolov5-det-blocks-master.zip

YOLOv1-YOLOv8论文及总结

YOLOv1-YOLOv5论文解读.pdf

yolov论文-参考意义不大.docx

yolov论文-具体详细教程.txt

yolov论文-基于YOLOv5s的水下生物识别算法研究

yolov论文-基于改进的YOLOv射线探伤缺陷检测方法

yolov1-yolov7总共7篇英文论文原文

yolov论文-基于YOLOv5算法的名优茶采摘机器人

最新资源