基于 YOLOv8 的无人机红外极小目标检测系统实战 | 极客日志

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基于 YOLOv8 的无人机红外极小目标检测系统实战

本方案分享一个基于 YOLOv8 构建的无人机红外图像极小目标检测系统。数据集包含 8302 张红外图像，涵盖飞机、无人机、直升机和鸟类四类目标，提供 YOLO 和 VOC 标注格式。系统采用 Flask 搭建 Web 界面，支持图像上传与实时检测可视化。代码经过整理修复，可直接用于低空安防、机场净空监测等场景的模型部署与推理验证。

kaikai发布于 2026/4/8更新于 2026/4/251 浏览

基于 YOLOv8 的无人机红外极小目标检测系统实战

基于 YOLOv8 的无人机红外极小目标检测系统实战

最近整理了一套无人机红外图像下的极小目标检测数据集，共 8302 张，涵盖了飞机、无人机、直升机和鸟类四类目标。这套数据特别适合低空安防、机场净空监测以及反无人机系统研发。今天把基于 YOLOv8 搭建的检测系统完整流程分享给大家，包括数据处理、模型推理以及 Flask 可视化的实现细节。

数据集概览

数据集提供了 YOLO (.txt) 和 PASCAL VOC (.xml) 两种标注格式，方便不同框架迁移。主要统计信息如下：

类别	数量
Plane (飞机)	2,163
Drone (无人机)	3,120
Heli (直升机)	2,217
Bird (鸟类)	1,958
总计	9,458

图像均为红外模态，目标特征明显偏小（通常小于 32×32 像素），这对模型的感知能力提出了更高要求。

数据集样本示例

项目架构与环境

为了快速落地验证，我们采用了轻量级的 Flask 后端配合 YOLOv8 核心推理模块。目录结构保持清晰，便于后续扩展：

infrared_small_target_detection_yolov8/
├── data/
│   └── dataset.yaml
├── models/
│   └── best.pt
├── static/
│   ├── uploads/
│   └── results/
├── templates/
│   ├── index.html
│   └── result.html
├── app.py
├── detect.py
├── requirements.txt
└── README.md

依赖包方面，主要使用 ultralytics 处理模型加载，opencv-python 做图像处理，flask 负责 Web 服务。安装命令很简单：

pip install -r requirements.txt

核心配置与逻辑

数据集配置文件 dataset.yaml 需要明确指定训练集路径及类别名称，确保模型能正确映射 ID 到中文标签。

train: 
 
 
 
 [, , , ]

import cv2
import os
from ultralytics import YOLO

CLASS_NAMES = {
    0: "飞机",
    1: "无人机",
    2: "直升机",
    3: "鸟类"
}

def run_detection(input_path, output_dir="static/results", model_path="models/best.pt"):
    """对单张红外图像进行极小目标检测"""
    model = YOLO(model_path)
    img = cv2.imread(input_path)
    if img is None:
        raise ValueError("无法读取图像，请检查路径")
    
    results = model(img, conf=0.4)
    annotated_img = img.copy()
    boxes = results[0].boxes
    
    class_counts = {name: 0 for name in CLASS_NAMES.values()}
    
    for box in boxes:
        x1, y1, x2, y2 = map(int, box.xyxy[0])
        conf = float(box.conf[0])
        cls_id = int(box.cls[0])
        
        if cls_id not in CLASS_NAMES:
            continue
        
        class_name = CLASS_NAMES[cls_id]
        class_counts[class_name] += 1
        
        color = (0, 255, 0)
        cv2.rectangle(annotated_img, (x1, y1), (x2, y2), color, 2)
        label = f"{class_name} {conf:.2f}"
        cv2.putText(annotated_img, label, (x1, y1 - 10), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
    
    os.makedirs(output_dir, exist_ok=True)
    filename = os.path.basename(input_path)
    output_path = os.path.join(output_dir, f"det_{filename}")
    cv2.imwrite(output_path, annotated_img)
    
    return output_path, class_counts

from flask import Flask, render_template, request, redirect, url_for
import os
from detect import run_detection

app = Flask(__name__)
UPLOAD_FOLDER = 'static/uploads'
RESULT_FOLDER = 'static/results'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}

app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['RESULT_FOLDER'] = RESULT_FOLDER

def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/upload', methods=['POST'])
def upload_file():
    if 'file' not in request.files:
        return redirect(request.url)
    file = request.files['file']
    if file.filename == '' or not allowed_file(file.filename):
        return redirect(request.url)

    filename = file.filename
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
    file.save(filepath)

    try:
        output_path, counts = run_detection(
            filepath, 
            output_dir=app.config['RESULT_FOLDER'], 
            model_path="models/best.pt"
        )
        result_filename = os.path.basename(output_path)
    except Exception as e:
        return f"<h2>检测失败：{str(e)}</h2>"

    non_zero_counts = {k: v for k, v in counts.items() if v > 0}
    return render_template('result.html', 
                           original=filename, 
                           detected=result_filename, 
                           counts=non_zero_counts)

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=5000)

<!-- templates/index.html -->
<!DOCTYPE html>
<html>
<head>
    <title>红外极小目标检测系统</title>
    <style>
        body { font-family: Arial; text-align: center; margin-top: 40px; }
        .btn { padding: 8px 16px; margin: 5px; background: #007bff; color: white; border: none; cursor: pointer; }
        .btn:hover { background: #0069d9; }
    </style>
</head>
<body>
    <h2>🔍 无人机红外极小目标检测系统</h2>
    <form method="post" enctype="multipart/form-data" action="/upload">
        <input type="file" name="file" accept="image/*" required><br><br>
        <button type="submit" class="btn">上传并检测</button>
    </form>
</body>
</html>

<!-- templates/result.html -->
<!DOCTYPE html>
<html>
<head>
    <title>检测结果</title>
    <style>
        body { font-family: Arial; margin: 20px; }
        img { max-width: 95%; margin: 10px; border: 1px solid #ddd; }
        .stats { background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }
        .item { display: inline-block; margin-right: 15px; }
    </style>
</head>
<body>
    <h2>📊 检测结果</h2>
    <img src="{{ url_for('static', filename='uploads/' + original) }}" alt="原始图">
    <img src="{{ url_for('static', filename='results/' + detected) }}" alt="检测结果">
    <div class="stats">
        <strong>检测到的目标：</strong> 
        {% for name, count in counts.items() %}
        <span class="item">{{ name }}: {{ count }} 处</span>
        {% endfor %}
    </div>
    <a href="/">← 返回上传</a>
</body>
</html>

python app.py