基于 YOLOv8 的反无人机红外检测系统构建与部署

三、模型训练与评估

1. 训练代码

首先，下载官方预训练模型（YOLOv8s）：

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt

然后，开始训练：

from ultralytics import YOLO

# 加载预训练模型
model = YOLO('yolov8s.pt')
# 开始训练
results = model.train(data='path/to/data.yaml', epochs=100, imgsz=640)

或者直接通过命令行执行：

yolo train data=path/to/data.yaml model=yolov8s.pt epochs=100 imgsz=640 batch=16 device=0

2. 推理代码

加载训练好的模型进行预测：

from ultralytics import YOLO
import cv2

# 加载训练好的模型
model = YOLO('runs/detect/train/weights/best.pt')

def detect_drone(image_path, model):
    # 使用模型进行预测
    results = model.predict(source=image_path, conf=0.25)
    # 可调整置信度阈值
    # 获取原始图像并绘制检测框
    for result in results:
        annotated_img = result.plot()
        # 自动绘制边界框和标签
        annotated_img = cv2.cvtColor(annotated_img, cv2.COLOR_RGB2BGR)
        # 转换颜色空间
        return annotated_img

# 示例调用并显示图像
result_image = detect_drone('test_images/drone.jpg', model)
cv2.imshow('Drone Detection Result', result_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

3. 评估代码

在验证集上评估模型性能：

from ultralytics import YOLO

# 加载训练好的模型
model = YOLO('runs/detect/train/weights/best.pt')
# 在验证集上评估模型性能
metrics = model.val(data='path/to/data.yaml')
print("mAP50:", metrics.box.map50)
print("mAP50-95:", metrics.box.map)
print("Precision:", metrics.box.precision)
print("Recall:", metrics.box.recall)

四、核心功能实现

你已经训练好了 YOLOv8 的无人机检测模型权重（如 best.pt），现在希望根据这些权重构建一个完整的无人机目标检测系统，包括实时检测、批量检测、可视化及结果保存等。

1. 项目结构建议

drone_detection_system/
├── weights/
│   └── best.pt
├── detect.py
├── utils/
│   └── plotting.py
├── input/
│   └── test.mp4
├── output/
│   └── result.mp4
├── config/
│   └── data.yaml
└── requirements.txt

2. 环境依赖

pip install ultralytics opencv-python numpy matplotlib

3. 加载模型（detect.py）

from ultralytics import YOLO

# 加载本地训练好的模型
model = YOLO('weights/best.pt')

4. 图像检测函数

import cv2

def detect_image(image_path, model, save_path=None):
    results = model(image_path)
    for r in results:
        im_array = r.plot()
        im = cv2.cvtColor(im_array, cv2.COLOR_RGB2BGR)
        if save_path:
            cv2.imwrite(save_path, im)
        cv2.imshow("Detection", im)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

5. 视频检测函数（支持摄像头、视频文件、RTSP）

def detect_video(video_source, model, save_path=None):
    cap = cv2.VideoCapture(video_source)
    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(save_path, fourcc, fps, (w, h)) if save_path else None
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        results = model(frame)
        for r in results:
            annotated_frame = r.plot()
            annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
            if out:
                out.write(annotated_frame)
        cv2.imshow('Drone Detection', annotated_frame)
        if cv2.waitKey(1) == 27:
            break
    cap.release()
    if out:
        out.release()
    cv2.destroyAllWindows()

6. 使用示例

if __name__ == '__main__':
    model = YOLO('weights/best.pt')
    # 图像检测
    detect_image('input/test.jpg', model, 'output/result.jpg')
    # 视频检测
    detect_video('input/test.mp4', model, 'output/result.mp4')
    # 实时摄像头检测
    detect_video(0, model, None)
    # RTSP 流检测
    detect_video('rtsp://admin:[email protected]:554', model, None)

五、部署方案建议

1. 本地桌面系统（Windows / Linux）

• 使用 PyQt / Tkinter 构建 GUI 界面
• 支持图像、视频、摄像头、RTSP 检测
• 支持结果保存与导出

2. Web 系统（Flask + YOLO）

• 使用 Flask 构建 Web 接口
• 前端上传图像/视频，后端返回检测结果
• 可用于远程无人机监控平台

3. 边缘设备部署（Jetson Nano / Xavier NX）

• 使用 TensorRT 加速推理
• 部署 .engine 模型文件
• 支持低功耗实时检测

4. Docker 容器化部署

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "detect.py"]

构建并运行：

docker build -t drone-detection .
docker run -it --rm --gpus all drone-detection

六、总结

附：完整 detect.py 示例

from ultralytics import YOLO
import cv2
import json

model = YOLO('weights/best.pt')

def detect_image(image_path, save_path=None):
    results = model(image_path)
    for r in results:
        im_array = r.plot()
        im = cv2.cvtColor(im_array, cv2.COLOR_RGB2BGR)
        if save_path:
            cv2.imwrite(save_path, im)
        cv2.imshow("Detection", im)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

def detect_video(video_source, save_path=None):
    cap = cv2.VideoCapture(video_source)
    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(save_path, fourcc, fps, (w, h)) if save_path else None
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        results = model(frame)
        for r in results:
            annotated_frame = r.plot()
            annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
            if out:
                out.write(annotated_frame)
        cv2.imshow('Drone Detection', annotated_frame)
        if cv2.waitKey(1) == 27:
            break
    cap.release()
    if out:
        out.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    detect_video('input/test.mp4', 'output/result.mp4')