fix: correct project structure and file path errors in README

- 修正项目根目录名称: 1BaseOnYOLO → 2BaseOnYOLO_InfraredData
- 移除不存在的脚本: download_dataset.py, retry_download.py
- 添加存在的脚本: analyze_temp_features.py
- 统一模型权重文件名: infrared_yolov8n.pt → best.pt

README.md

@@ -21,7 +21,7 @@
 ## 项目结构
 
 ```
-1BaseOnYOLO/
+2BaseOnYOLO_InfraredData/
 ├── src/                        # 核心源代码
 │   ├── detect.py              # YOLO目标检测模块
 │   ├── extract_temp.py        # ROI温度特征提取
@@ -31,10 +31,9 @@
 ├── scripts/                    # 工具脚本
 │   ├── train.py              # 模型训练/验证入口
 │   ├── test_cuda.py          # CUDA环境测试
-│   ├── download_dataset.py    # 数据集下载
 │   ├── convert_to_yolo.py     # 数据集格式转换
-│   ├── retry_download.py      # 下载失败重试
-│   └── usage_example.py       # 使用示例
+│   ├── usage_example.py       # 使用示例
+│   └── analyze_temp_features.py # ROI温度特征分析
 ├── data/                      # 数据目录
 │   ├── dataset.yaml          # YOLO数据集配置
 │   ├── train/                # 训练集
@@ -48,7 +47,7 @@
 │   │   └── labels/           # 测试标签
 │   └── 标签.txt              # 类别标签说明
 ├── weights/                   # 模型权重
-│   └── infrared_yolov8n.pt   # 训练好的模型
+│   └── best.pt              # 训练好的模型
 ├── pyproject.toml            # uv项目配置
 └── README.md                  # 项目文档
 ```
@@ -191,7 +190,7 @@ uv run python scripts/usage_example.py
 from src.detect import InfraredDetector
 
 # 初始化检测器
-detector = InfraredDetector("weights/infrared_yolov8n.pt", conf=0.25)
+detector = InfraredDetector("weights/best.pt", conf=0.25)
 
 # 检测图像
 detections = detector.detect("path/to/image.jpg")
@@ -227,7 +226,7 @@ detector.detect_and_draw(
 from src.extract_temp import TempFeatureExtractor
 import cv2
 
-extractor = TempFeatureExtractor("weights/infrared_yolov8n.pt")
+extractor = TempFeatureExtractor("weights/best.pt")
 
 # 读取红外图像
 gray_img = cv2.imread("path/to/image.jpg", cv2.IMREAD_GRAYSCALE)
@@ -259,7 +258,7 @@ print(f"温度标准差: {features['temp_std']}")
 ```python
 from src.diagnose import FaultDiagnoser
 
-diagnoser = FaultDiagnoser("weights/infrared_yolov8n.pt")
+diagnoser = FaultDiagnoser("weights/best.pt")
 result = diagnoser.diagnose("path/to/image.jpg")
 
 print(f"诊断状态: {result['summary']['status']}")
@@ -325,7 +324,7 @@ threshold = {
 }
 
 diagnoser = FaultDiagnoser(
-    "weights/infrared_yolov8n.pt",
+    "weights/best.pt",
     temp_threshold=threshold
 )
 ```
@@ -397,14 +396,14 @@ uv run python scripts/train.py train \
 ### 验证模型
 ```bash
 uv run python scripts/train.py validate \
-    --model weights/infrared_yolov8n.pt \
+    --model weights/best.pt \
     --data data/dataset.yaml
 ```
 
 ### 导出模型
 ```bash
 uv run python scripts/train.py export \
-    --model weights/infrared_yolov8n.pt \
+    --model weights/best.pt \
     --format onnx
 ```