基于YOLOv8开发构建目标检测模型超详细教程【以焊缝质量检测数据场景为例】

**基于YOLOv8开发构建目标检测模型超详细教程**

**一、前言**

目标检测是计算机视觉领域的一个重要任务，它的目的是从图像或视频中识别出特定的目标。YOLO（You Only Look Once）是一种流行的目标检测算法，能够快速准确地检测出多个目标。YOLOv8是YOLO系列中的最新版本，具有更好的性能和效率。

在本教程中，我们将使用YOLOv8开发一个目标检测模型，以焊缝质量检测数据场景为例。我们将详细介绍如何准备数据、训练模型、测试模型等步骤。

**二、准备数据**

首先，我们需要准备一个包含焊缝图像的数据集。假设我们的数据集名为`flaw_dataset`，包含两类图像：正常焊缝和缺陷焊缝。

我们可以使用以下代码来读取数据并将其分成训练集、验证集和测试集：

import osfrom torch.utils.data import Dataset, DataLoaderclass FlawDataset(Dataset):
 def __init__(self, data_dir, transform=None):
 self.data_dir = data_dir self.transform = transform self.images = []
 for file in os.listdir(data_dir):
 if file.endswith('.jpg'):
 self.images.append(os.path.join(data_dir, file))

 def __len__(self):
 return len(self.images)

 def __getitem__(self, index):
 image_path = self.images[index]
 image = cv2.imread(image_path)
 label =0 # 正常焊缝 if '缺陷' in image_path:
 label =1 # 缺陷焊缝 if self.transform is not None:
 image = self.transform(image)

 return image, labeltrain_dataset = FlawDataset('data/train')
val_dataset = FlawDataset('data/val')
test_dataset = FlawDataset('data/test')

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

import torchfrom torchvision import modelsclass YOLOv8Model(torch.nn.Module):
 def __init__(self):
 super(YOLOv8Model, self).__init__()
 self.backbone = models.resnet50(pretrained=True)
 self.head = torch.nn.Sequential(
 torch.nn.Conv2d(512,256, kernel_size=3),
 torch.nn.ReLU(),
 torch.nn.MaxPool2d(kernel_size=2, stride=2),
 torch.nn.Flatten()
 )
 self.fc = torch.nn.Linear(256,2)

 def forward(self, x):
 x = self.backbone(x)
 x = self.head(x)
 x = self.fc(x)
 return x

model = YOLOv8Model()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
 for batch in train_loader:
 images, labels = batch optimizer.zero_grad()
 outputs = model(images)
 loss = criterion(outputs, labels)
 loss.backward()
 optimizer.step()

 print(f'Epoch {epoch+1}, Loss: {loss.item()}')

model.eval()
test_loss =0correct =0with torch.no_grad():
 for batch in test_loader:
 images, labels = batch outputs = model(images)
 loss = criterion(outputs, labels)
 test_loss += loss.item()
 _, predicted = torch.max(outputs,1)
 correct += (predicted == labels).sum().item()

accuracy = correct / len(test_dataset)
print(f'Test Loss: {test_loss / len(test_loader)}')
print(f'Accuracy: {accuracy:.2f}%')