火灾检测模型由基于YOLOv10的火灾烟雾检测模型改为复合模型[基于YOLOv8的火灾检测模型（单火焰检测）＋YOLOv10-M，专用火灾烟雾模型]

apps/server/api/detection.py

@@ -20,7 +20,8 @@ async def detect_image(
     model_id: str = Query("fire_detection"),
     confidence: float = Query(0.5),
     iou: float = Query(0.45),
-    algorithm_config: Optional[str] = Query(None, description="算法配置JSON字符串")
+    algorithm_config: Optional[str] = Query(None, description="算法配置JSON字符串"),
+    composite: bool = Query(False, description="是否启用复合检测（火灾检测时同时检测火焰和烟雾）")
 ):
     """
     图片检测接口
@@ -61,9 +62,15 @@ async def detect_image(
                 data={}
             )
         
-        result = await detection_service.detect_image(
+        # 判断是否启用复合火灾检测
-            frame, model_id, confidence, iou, algorithm_config=algo_config
+        if composite and model_id == 'fire_detection':
-        )
+            result = await detection_service.detect_fire_composite(
+                frame, confidence, iou
+            )
+        else:
+            result = await detection_service.detect_image(
+                frame, model_id, confidence, iou, algorithm_config=algo_config
+            )
         
         if result['success']:
             annotated_frame = detection_service.draw_detections(

apps/server/services/camera_service.py

@@ -235,13 +235,15 @@ class CameraService:
                 confidence = config.get('confidence', 0.5)
                 iou = config.get('iou', 0.45)
                 draw = True
+                composite = config.get('composite', False)
                 
                 processed_frame, result = await detection_service.detect_frame(
                     frame,
                     model_id=model_id,
                     confidence=confidence,
                     iou=iou,
-                    draw=draw
+                    draw=draw,
+                    composite=composite
                 )
                 
                 if result['success']:

apps/server/services/detection_service.py

@@ -4,10 +4,11 @@ import numpy as np
 import time
 import uuid
 import logging
-import torch
 from typing import Dict, List, Optional
 from PIL import Image, ImageDraw, ImageFont
 
+import torch
+
 from .loitering_service import get_loitering_service
 
 logger = logging.getLogger(__name__)
@@ -148,19 +149,39 @@ class DetectionService:
         model_id: str,
         confidence: float = 0.5,
         iou: float = 0.45,
-        draw: bool = True
+        draw: bool = True,
+        composite: bool = False
     ) -> tuple:
         start_time = time.time()
         
-        model = await self.model_service.load_model(model_id)
-        if not model:
-            return frame, {
-                'success': False,
-                'detections': [],
-                'stats': None
-            }
-        
         try:
+            # 如果是火灾检测模型且启用了复合检测
+            if composite and model_id == 'fire_detection':
+                result_data = await self.detect_fire_composite(frame, confidence=confidence, iou=iou)
+                
+                if result_data['success']:
+                    detections = result_data['detections']
+                    processing_time = time.time() - start_time
+                    fps = 1.0 / processing_time if processing_time > 0 else 0
+                    
+                    # 更新 stats 中的 fps 和处理时间
+                    result_data['stats']['fps'] = round(fps, 2)
+                    result_data['stats']['processing_time'] = round(processing_time, 3)
+                    
+                    if draw:
+                        frame = self.draw_detections(frame, detections, fps)
+                
+                return frame, result_data
+            
+            # 普通单模型检测
+            model = await self.model_service.load_model(model_id)
+            if not model:
+                return frame, {
+                    'success': False,
+                    'detections': [],
+                    'stats': None
+                }
+            
             results = model(frame, conf=confidence, iou=iou, verbose=False)
             
             detections = []
@@ -282,6 +303,115 @@ class DetectionService:
                 'stats': None
             }
     
+    async def detect_fire_composite(
+        self,
+        image: np.ndarray,
+        confidence: float = 0.1,
+        iou: float = 0.45
+    ) -> Dict:
+        """
+        复合火灾检测：同时检测火焰和烟雾
+        """
+        start_time = time.time()
+        
+        try:
+            # 1. 检测火焰
+            fire_model = await self.model_service.load_model('fire_detection')
+            fire_results = fire_model(image, conf=confidence, iou=iou, verbose=False)
+            
+            fire_detections = []
+            for result in fire_results:
+                for box in result.boxes:
+                    try:
+                        xyxy_values = box.xyxy.squeeze().tolist()
+                        if len(xyxy_values) >= 4:
+                            x1, y1, x2, y2 = float(xyxy_values[0]), float(xyxy_values[1]), float(xyxy_values[2]), float(xyxy_values[3])
+                        else:
+                            continue
+                        
+                        conf = float(box.conf[0]) if hasattr(box.conf, '__getitem__') else float(box.conf)
+                        cls = int(box.cls[0]) if hasattr(box.cls, '__getitem__') else int(box.cls)
+                        class_name = result.names[cls]
+                        
+                        if class_name == 'Fire':
+                            fire_detections.append({
+                                'class': 'Fire',
+                                'label': '火焰',
+                                'confidence': round(conf, 3),
+                                'bbox': [int(x1), int(y1), int(x2), int(y2)],
+                                'type': 'fire'
+                            })
+                    except Exception as e:
+                        logger.error(f"火焰检测解析失败: {e}")
+                        continue
+            
+            # 2. 检测烟雾（使用YOLOv10-M专用火灾烟雾模型，只保留 Smoke 类别）
+            smoke_model = await self.model_service.load_model('smoke_detection')
+            smoke_results = smoke_model(image, conf=confidence, iou=iou, verbose=False)
+            
+            smoke_detections = []
+            for result in smoke_results:
+                for box in result.boxes:
+                    try:
+                        xyxy_values = box.xyxy.squeeze().tolist()
+                        if len(xyxy_values) >= 4:
+                            x1, y1, x2, y2 = float(xyxy_values[0]), float(xyxy_values[1]), float(xyxy_values[2]), float(xyxy_values[3])
+                        else:
+                            continue
+                        
+                        conf = float(box.conf[0]) if hasattr(box.conf, '__getitem__') else float(box.conf)
+                        cls = int(box.cls[0]) if hasattr(box.cls, '__getitem__') else int(box.cls)
+                        class_name = result.names[cls]
+                        
+                        # 只保留 Smoke 类别（注意模型输出为大写 S）
+                        if class_name == 'Smoke':
+                            smoke_detections.append({
+                                'class': 'Smoke',
+                                'label': '烟雾',
+                                'confidence': round(conf, 3),
+                                'bbox': [int(x1), int(y1), int(x2), int(y2)],
+                                'type': 'smoke'
+                            })
+                    except Exception as e:
+                        logger.error(f"烟雾检测解析失败: {e}")
+                        continue
+            
+            # 3. 合并所有检测
+            all_detections = fire_detections + smoke_detections
+            
+            # 4. 判定是否疑似火灾（火焰或烟雾任一检测到即判定为是）
+            suspected_fire = len(fire_detections) > 0 or len(smoke_detections) > 0
+            
+            processing_time = time.time() - start_time
+            avg_confidence = sum(d['confidence'] for d in all_detections) / len(all_detections) if all_detections else 0
+            
+            return {
+                'success': True,
+                'message': '复合火灾检测完成',
+                'detections': all_detections,
+                'stats': {
+                    'total_detections': len(all_detections),
+                    'fire_count': len(fire_detections),
+                    'smoke_count': len(smoke_detections),
+                    'avg_confidence': round(avg_confidence, 3),
+                    'suspected_fire': suspected_fire,
+                    'suspected_fire_label': '是' if suspected_fire else '否',
+                    'processing_time': round(processing_time, 3),
+                    'model_used': 'fire_composite'
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"复合火灾检测失败: {e}")
+            import traceback
+            logger.error(f"错误堆栈: {traceback.format_exc()}")
+            return {
+                'success': False,
+                'message': f'复合火灾检测失败: {str(e)}',
+                'detections': [],
+                'stats': None
+            }
+
     def _apply_behavior_analysis(
         self,
         result_data: Dict,

apps/web/src/components/DetectionConfig.vue

@@ -117,6 +117,36 @@
           <div class="slider-value">{{ config.iou.toFixed(2) }}</div>
         </el-form-item>
 
+        <!-- 复合检测开关（仅对火灾检测模型显示） -->
+        <el-form-item v-if="isFireDetectionModel">
+          <template #label>
+            <span>复合火灾检测</span>
+            <el-tooltip placement="top" :show-after="200">
+              <template #content>
+                <div style="max-width: 300px; line-height: 1.6;">
+                  <p><strong>复合火灾检测是什么？</strong></p>
+                  <p>同时检测火焰和烟雾，提高火灾识别准确率</p>
+                  <p style="margin: 8px 0;"><strong>检测内容：</strong></p>
+                  <ul style="margin: 8px 0; padding-left: 16px;">
+                    <li>火焰检测：识别明火区域</li>
+                    <li>烟雾检测：识别烟雾区域</li>
+                    <li>综合判断：根据两者结果评估火灾等级</li>
+                  </ul>
+                  <p><strong>建议：火灾检测场景建议开启</strong></p>
+                </div>
+              </template>
+              <el-icon class="help-icon"><QuestionFilled /></el-icon>
+            </el-tooltip>
+          </template>
+          <el-switch
+            v-model="config.composite"
+            active-text="开启"
+            inactive-text="关闭"
+            :active-value="true"
+            :inactive-value="false"
+          />
+        </el-form-item>
+
         <!-- 算法配置（仅对人员检测模型显示） -->
         <AlgorithmConfig 
           v-model="config.algorithmConfig" 
@@ -129,7 +159,7 @@
 </template>
 
 <script setup>
-import { ref, watch } from 'vue'
+import { ref, watch, computed } from 'vue'
 import {
   Setting,
   Picture,
@@ -167,9 +197,15 @@ const config = ref({
   model: props.defaultModel || (props.models.length > 0 ? props.models[0].id : ''),
   confidence: 0.5,
   iou: 0.45,
+  composite: false,
   algorithmConfig: {}
 })
 
+// 判断当前是否是火灾检测模型
+const isFireDetectionModel = computed(() => {
+  return config.value.model === 'fire_detection'
+})
+
 const formatConfidence = (value) => {
   return `置信度: ${value.toFixed(2)}`
 }
@@ -191,9 +227,16 @@ watch(configType, (newType) => {
 })
 
 // 监听配置变化
-watch(() => [config.value.model, config.value.confidence, config.value.iou], () => {
+watch(() => [config.value.model, config.value.confidence, config.value.iou, config.value.composite], () => {
   emit('config-change', config.value)
 }, { deep: true })
+
+// 监听模型变化，如果不是火灾模型，关闭复合检测
+watch(() => config.value.model, (newModel) => {
+  if (newModel !== 'fire_detection') {
+    config.value.composite = false
+  }
+})
 </script>
 
 <style scoped>

apps/web/src/components/VideoDetection.vue

@@ -232,6 +232,7 @@ const config = ref({
   model: props.models.length > 0 ? props.models[0].id : 'fire_detection',
   confidence: 0.5,
   iou: 0.45,
+  composite: false,
   algorithmConfig: {}
 })
 
@@ -346,7 +347,8 @@ const startCamera = async () => {
         config: {
           model_id: config.value.model,
           confidence: config.value.confidence,
-          iou: config.value.iou
+          iou: config.value.iou,
+          composite: config.value.composite
         }
       }
 
@@ -416,7 +418,8 @@ const updateCameraConfig = () => {
       config: {
         model_id: config.value.model,
         confidence: config.value.confidence,
-        iou: config.value.iou
+        iou: config.value.iou,
+        composite: config.value.composite
       }
     }