基于R-CNN深度学习的舰船检测与识别系统：数据集、模型和UI界面的完整实现

 from torch.utils.data import Dataset, DataLoader

    
 import os
    
 import cv2
    
 import torch
    
  
    
 class ShipDataset(Dataset):
    
     def __init__(self, annotations, img_dir, transform=None):
    
     self.annotations = annotations
    
     self.img_dir = img_dir
    
     self.transform = transform
    
     
    
     def __getitem__(self, idx):
    
     img_path = os.path.join(self.img_dir, self.annotations[idx]['filename'])
    
     image = cv2.imread(img_path)
    
     boxes = self.annotations[idx]['boxes']
    
     labels = self.annotations[idx]['labels']
    
     
    
     # 转换为Tensor
    
     boxes = torch.as_tensor(boxes, dtype=torch.float32)
    
     labels = torch.as_tensor(labels, dtype=torch.int64)
    
     
    
     target = {'boxes': boxes, 'labels': labels}
    
     
    
     if self.transform:
    
         image = self.transform(image)
    
     
    
     return image, target
    
     
    
     def __len__(self):
    
     return len(self.annotations)
    
  
    
 # 使用DataLoader
    
 train_loader = DataLoader(dataset=ShipDataset(train_annotations, img_dir, transform=transform),
    
                       batch_size=4, shuffle=True, num_workers=2)
    
  
    
    
    
    
    代码解释

 import torchvision

    
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
    
  
    
 # 加载预训练的 Faster R-CNN 模型
    
 model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
    
  
    
 # 替换分类头，以适应新类别数量
    
 num_classes = 2  # 1 类别（舰船）+ 背景
    
 in_features = model.roi_heads.box_predictor.cls_score.in_features
    
 model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
    
    
    
    
    代码解释

 import torch

    
 import torch.optim as optim
    
  
    
 # 定义优化器
    
 optimizer = optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=0.0005)
    
  
    
 # 设置训练参数
    
 num_epochs = 10
    
 device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    
 model.to(device)
    
  
    
 # 训练循环
    
 for epoch in range(num_epochs):
    
     model.train()
    
     epoch_loss = 0
    
     for images, targets in train_loader:
    
     images = [img.to(device) for img in images]
    
     targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
    
  
    
     loss_dict = model(images, targets)
    
     losses = sum(loss for loss in loss_dict.values())
    
     
    
     optimizer.zero_grad()
    
     losses.backward()
    
     optimizer.step()
    
     
    
     epoch_loss += losses.item()
    
     
    
     print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}")
    
    
    
    
    代码解释

 model.eval()

    
 with torch.no_grad():
    
     for images, targets in val_loader:
    
     images = [img.to(device) for img in images]
    
     targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
    
  
    
     # 获得模型预测结果
    
     outputs = model(images)
    
     
    
     # 计算评价指标（如准确率、召回率）
    
    
    
    
    代码解释

 import sys

    
 from PyQt5.QtWidgets import QApplication, QMainWindow, QPushButton, QLabel, QFileDialog
    
 from PyQt5.QtGui import QPixmap, QImage
    
 import cv2
    
  
    
 class ShipDetectionUI(QMainWindow):
    
     def __init__(self):
    
     super().__init__()
    
     self.setWindowTitle("舰船检测系统")
    
     self.setGeometry(200, 200, 800, 600)
    
     
    
     # 上传按钮
    
     self.upload_btn = QPushButton("上传图像", self)
    
     self.upload_btn.setGeometry(50, 50, 100, 30)
    
     self.upload_btn.clicked.connect(self.upload_image)
    
     
    
     # 检测按钮
    
     self.detect_btn = QPushButton("检测", self)
    
     self.detect_btn.setGeometry(50, 100, 100, 30)
    
     self.detect_btn.clicked.connect(self.detect_image)
    
     
    
     # 保存按钮
    
     self.save_btn = QPushButton("保存结果", self)
    
     self.save_btn.setGeometry(50, 150, 100, 30)
    
     self.save_btn.clicked.connect(self.save_image)
    
     
    
     # 图像显示区域
    
     self.image_label = QLabel(self)
    
     self.image_label.setGeometry(200, 50, 500, 500)
    
     self.image_label.setStyleSheet("border: 1px solid black;")
    
     
    
     def upload_image(self):
    
     # 上传图像
    
     file_path, _ = QFileDialog.getOpenFileName(self, "选择图像", "", "Images (*.png *.jpg)")
    
     if file_path:
    
         self.image_path = file_path
    
         self.show_image(file_path)
    
     
    
     def show_image(self, img_path):
    
     # 显示上传的图像
    
     pixmap = QPixmap(img_path)
    
     self.image_label.setPixmap(pixmap.scaled(self.image_label.size()))
    
     
    
     def detect_image(self):
    
     # 调用检测模型并显示结果
    
     # 此处应实现模型加载和推理的逻辑
    
     pass
    
     
    
     def save_image(self):
    
     # 保存检测结果
    
     pass
    
  
    
 if __name__ == '__main__':
    
     app = QApplication(sys.argv)
    
     window = ShipDetectionUI()
    
     window.show()
    
     sys.exit(app.exec_())
    
    
    
    
    代码解释

 import torch

    
  
    
 # 将模型转换为 TorchScript 格式
    
 traced_model = torch.jit.trace(model, torch.rand(1, 3, 224, 224))
    
 traced_model.save("faster_rcnn_model.pt")
    
    
    
    
    代码解释