💡💡💡本文摘要：基于YOLO11的打篮球跟踪系统，阐述了整个数据制作和训练可视化过程
在当前挑战背景下，航天器检查是指对在轨航天器进行细致检测以评估其状态与功能的过程。现有最常用的检测方法往往依赖宇航员执行巡检任务（既耗时又危险），或使用昂贵笨重的设备（如激光雷达传感器和机械臂）。而本次挑战探索的创新方案采用了一种小型检查飞行器（又称"追踪器"），从主航天器部署后对主体航天器实施检测。与现有方法相比，这种检查飞行器成本相对低廉，采用轻量化的廉价摄像头和商用现货硬件。
本挑战着重旨在攻克航天器检测中的两大操作难题：首先，挑战数据集中的图像要求解决方案能适配多种未知且可能受损的航天器类型；其次，解决方案必须在我们的代码执行平台中运行，该平台模拟了搭载于NASA R5航天器上的商用现货计算机板环境——该航天器正是用于验证检测技术的演示平台。
博主简介
AI小怪兽，YOLO骨灰级玩家，1）YOLOv5、v7、v8、v9、v10、11优化创新，轻松涨点和模型轻量化；2）目标检测、语义分割、OCR、分类等技术孵化，赋能智能制造，工业项目落地经验丰富；
1.YOLO11介绍Ultralytics YOLO11是一款尖端的、最先进的模型，它在之前YOLO版本成功的基础上进行了构建，并引入了新功能和改进，以进一步提升性能和灵活性。YOLO11设计快速、准确且易于使用，使其成为各种物体检测和跟踪、实例分割、图像分类以及姿态估计任务的绝佳选择。
结构图如下：
1.1 C3k2C3k2，结构图如下
C3k2，继承自类C2f，其中通过c3k设置False或者Ture来决定选择使用C3k还是Bottleneck
实现代码ultralytics/nn/modules/block.py
代码语言：python复制class C3k2(C2f):
"""Faster Implementation of CSP Bottleneck with 2 convolutions."""
def __init__(self, c1, c2, n=1, c3k=False, e=0.5, g=1, shortcut=True):
"""Initializes the C3k2 module, a faster CSP Bottleneck with 2 convolutions and optional C3k blocks."""
super().__init__(c1, c2, n, shortcut, g, e)
self.m = nn.ModuleList(
C3k(self.c, self.c, 2, shortcut, g) if c3k else Bottleneck(self.c, self.c, shortcut, g) for _ in range(n)
)
class C3k(C3):
"""C3k is a CSP bottleneck module with customizable kernel sizes for feature extraction in neural networks."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, k=3):
"""Initializes the C3k module with specified channels, number of layers, and configurations."""
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
# self.m = nn.Sequential(*(RepBottleneck(c_, c_, shortcut, g, k=(k, k), e=1.0) for _ in range(n)))
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=(k, k), e=1.0) for _ in range(n)))
1.2 C2PSA介绍借鉴V10 PSA结构，实现了C2PSA和C2fPSA，最终选择了基于C2的C2PSA（可能涨点更好？）
实现代码ultralytics/nn/modules/block.py
代码语言：python复制
class PSABlock(nn.Module):
"""
PSABlock class implementing a Position-Sensitive Attention block for neural networks.
This class encapsulates the functionality for applying multi-head attention and feed-forward neural network layers
with optional shortcut connections.
Attributes:
attn (Attention): Multi-head attention module.
ffn (nn.Sequential): Feed-forward neural network module.
add (bool): Flag indicating whether to add shortcut connections.
Methods:
forward: Performs a forward pass through the PSABlock, applying attention and feed-forward layers.
Examples:
Create a PSABlock and perform a forward pass
>>> psablock = PSABlock(c=128, attn_ratio=0.5, num_heads=4, shortcut=True)
>>> input_tensor = torch.randn(1, 128, 32, 32)
>>> output_tensor = psablock(input_tensor)
"""
def __init__(self, c, attn_ratio=0.5, num_heads=4, shortcut=True) -> None:
"""Initializes the PSABlock with attention and feed-forward layers for enhanced feature extraction."""
super().__init__()
self.attn = Attention(c, attn_ratio=attn_ratio, num_heads=num_heads)
self.ffn = nn.Sequential(Conv(c, c * 2, 1), Conv(c * 2, c, 1, act=False))
self.add = shortcut
def forward(self, x):
"""Executes a forward pass through PSABlock, applying attention and feed-forward layers to the input tensor."""
x = x + self.attn(x) if self.add else self.attn(x)
x = x + self.ffn(x) if self.add else self.ffn(x)
return x
class C2PSA(nn.Module):
"""
C2PSA module with attention mechanism for enhanced feature extraction and processing.
This module implements a convolutional block with attention mechanisms to enhance feature extraction and processing
capabilities. It includes a series of PSABlock modules for self-attention and feed-forward operations.
Attributes:
c (int): Number of hidden channels.
cv1 (Conv): 1x1 convolution layer to reduce the number of input channels to 2*c.
cv2 (Conv): 1x1 convolution layer to reduce the number of output channels to c.
m (nn.Sequential): Sequential container of PSABlock modules for attention and feed-forward operations.
Methods:
forward: Performs a forward pass through the C2PSA module, applying attention and feed-forward operations.
Notes:
This module essentially is the same as PSA module, but refactored to allow stacking more PSABlock modules.
Examples:
>>> c2psa = C2PSA(c1=256, c2=256, n=3, e=0.5)
>>> input_tensor = torch.randn(1, 256, 64, 64)
>>> output_tensor = c2psa(input_tensor)
"""
def __init__(self, c1, c2, n=1, e=0.5):
"""Initializes the C2PSA module with specified input/output channels, number of layers, and expansion ratio."""
super().__init__()
assert c1 == c2
self.c = int(c1 * e)
self.cv1 = Conv(c1, 2 * self.c, 1, 1)
self.cv2 = Conv(2 * self.c, c1, 1)
self.m = nn.Sequential(*(PSABlock(self.c, attn_ratio=0.5, num_heads=self.c // 64) for _ in range(n)))
def forward(self, x):
"""Processes the input tensor 'x' through a series of PSA blocks and returns the transformed tensor."""
a, b = self.cv1(x).split((self.c, self.c), dim=1)
b = self.m(b)
return self.cv2(torch.cat((a, b), 1))
class C2fPSA(C2f):
"""
C2fPSA module with enhanced feature extraction using PSA blocks.
This class extends the C2f module by incorporating PSA blocks for improved attention mechanisms and feature extraction.
Attributes:
c (int): Number of hidden channels.
cv1 (Conv): 1x1 convolution layer to reduce the number of input channels to 2*c.
cv2 (Conv): 1x1 convolution layer to reduce the number of output channels to c.
m (nn.ModuleList): List of PSA blocks for feature extraction.
Methods:
forward: Performs a forward pass through the C2fPSA module.
forward_split: Performs a forward pass using split() instead of chunk().
Examples:
>>> import torch
>>> from ultralytics.models.common import C2fPSA
>>> model = C2fPSA(c1=64, c2=64, n=3, e=0.5)
>>> x = torch.randn(1, 64, 128, 128)
>>> output = model(x)
>>> print(output.shape)
"""
def __init__(self, c1, c2, n=1, e=0.5):
"""Initializes the C2fPSA module, a variant of C2f with PSA blocks for enhanced feature extraction."""
assert c1 == c2
super().__init__(c1, c2, n=n, e=e)
self.m = nn.ModuleList(PSABlock(self.c, attn_ratio=0.5, num_heads=self.c // 64) for _ in range(n))
1.3 11 Detect介绍分类检测头引入了DWConv（更加轻量级，为后续二次创新提供了改进点），结构图如下（和V8的区别）：
实现代码ultralytics/nn/modules/head.py
代码语言：python复制
self.cv2 = nn.ModuleList(
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
)
self.cv3 = nn.ModuleList(
nn.Sequential(
nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
nn.Conv2d(c3, self.nc, 1),
)
for x in ch
)
2.打篮球跟踪系统 为训练YOLO模型创建的数据集包含三个类别——球、球员、裁判。该数据集融合了多个数据集，以弥补某些类别数据量不足的问题。
2.1打篮球跟踪数据集介绍数据集大小：训练集29842张，验证集923张，测试集931张
类别3类：
代码语言：javascript复制names:
- ball
- player
- ref
nc: 3细节图：
标签可视化分析
2.2 配置Basketball.yamlps:建议填写绝对路径
代码语言：javascript复制path: D:/YOLOv11/data/Basketball-data
names:
- ball
- player
- ref
nc: 3
test: ./test/images
train: ./train/images
val: ./valid/images2.3 如何训练代码语言：javascript复制import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO
if __name__ == '__main__':
model = YOLO('ultralytics/cfg/models/11/yolo11.yaml')
model.train(data='data/Basketball.yaml',
cache=False,
imgsz=640,
epochs=200,
batch=8,
close_mosaic=10,
device='0',
optimizer='SGD', # using SGD
project='runs/train',
name='exp',
)2.4 训练结果可视化结果代码语言：javascript复制YOLO11 summary (fused): 341 layers, 3,265,879 parameters, 0 gradients, 7.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 29/29 [00:14<00:00, 2.03it/s]
all 923 10805 0.944 0.9 0.948 0.667
ball 732 740 0.914 0.75 0.869 0.469
player 923 8049 0.971 0.975 0.99 0.77
ref 894 2016 0.947 0.976 0.984 0.763预测结果：
3. 打篮球跟踪系统设计3.1 PySide6介绍 受益于人工智能的崛起，Python语言几乎以压倒性优势在众多编程语言中异军突起，成为AI时代的首选语言。在很多情况下，我们想要以图形化方式将我们的人工智能算法打包提供给用户使用，这时候选择以python为主的GUI框架就非常合适了。
PySide是Qt公司的产品，PyQt是第三方公司的产品，二者用法基本相同，不过在使用协议上却有很大差别。PySide可以在LGPL协议下使用，PyQt则在GPL协议下使用。
PySide目前常见的有两个版本：PySide2和PySide6。PySide2由C++版的Qt5开发而来.，而PySide6对应的则是C++版的Qt6。从PySide6开始，PySide的命名也会与Qt的大版本号保持一致，不会再出现类似PySide2对应Qt5这种容易混淆的情况。
3.2 安装PySide6代码语言：javascript复制pip install --upgrade pip
pip install pyside6 -i https://mirror.baidu.com/pypi/simple基于PySide6开发GUI程序包含下面三个基本步骤：
设计GUI，图形化拖拽或手撸；响应UI的操作（如点击按钮、输入数据、服务器更新），使用信号与Slot连接界面和业务；打包发布；3.3 打篮球跟踪系统设计