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创建用于室内和室外火灾检测的定制InceptionV3和CNN架构。

嵌入式处理技术的最新进展已使基于视觉的系统可以在监视过程中使用卷积神经网络检测火灾。在本文中，两个定制的CNN模型已经实现，它们拥有用于监视视频的高成本效益的火灾检测CNN架构。第一个模型是受AlexNet架构启发定制的基本CNN架构。我们将实现和查看其输出和限制，并创建一个定制的InceptionV3模型。为了平衡效率和准确性，考虑到目标问题和火灾数据的性质对模型进行了微调。我们将使用三个不同的数据集来训练我们的模型。

  
    
  
  
  

   
   
     
   
   
   
import tensorflow as tfimport keras_preprocessingfrom keras_preprocessing import imagefrom keras_preprocessing.image import ImageDataGeneratorTRAINING_DIR = "Train"training_datagen = ImageDataGenerator(rescale = 1./255,                                  horizontal_flip=True,                                  rotation_range=30,                                  height_shift_range=0.2,                                  fill_mode='nearest')VALIDATION_DIR = "Validation"validation_datagen = ImageDataGenerator(rescale = 1./255)train_generator = training_datagen.flow_from_directory(TRAINING_DIR,                                         target_size=(224,224),                                        class_mode='categorical',                                         batch_size = 64)validation_generator = validation_datagen.flow_from_directory(                                                VALIDATION_DIR,                                           target_size=(224,224),                                          class_mode='categorical',                                           batch_size= 16)
  
    
  
  
  

  
    
  
  
  

   
   
     
   
   
   
from tensorflow.keras.optimizers import Adammodel = tf.keras.models.Sequential([tf.keras.layers.Conv2D(96, (11,11), strides=(4,4), activation='relu', input_shape=(224, 224, 3)), tf.keras.layers.MaxPooling2D(pool_size = (3,3), strides=(2,2)),tf.keras.layers.Conv2D(256, (5,5), activation='relu'),tf.keras.layers.MaxPooling2D(pool_size = (3,3), strides=(2,2)),tf.keras.layers.Conv2D(384, (5,5), activation='relu'),tf.keras.layers.MaxPooling2D(pool_size = (3,3), strides=(2,2)),tf.keras.layers.Flatten(),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(2048, activation='relu'),tf.keras.layers.Dropout(0.25),tf.keras.layers.Dense(1024, activation='relu'),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(2, activation='softmax')])model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001),metrics=['acc'])history = model.fit(train_generator,steps_per_epoch = 15,epochs = 50,validation_data = validation_generator,validation_steps = 15)
  
    
  
  
  

我们开始为自定义的InceptionV3创建ImageDataGenerator。数据集包含3个类，但对于本文，我们将仅使用2个类。它包含用于训练的1800张图像和用于验证的200张图像。另外，我添加了8张客厅图像，以在数据集中添加一些噪点。

  
    
  
  
  

   
   
     
   
   
   
import tensorflow as tfimport keras_preprocessingfrom keras_preprocessing import imagefrom keras_preprocessing.image import ImageDataGeneratorTRAINING_DIR = "Train"training_datagen = ImageDataGenerator(rescale=1./255,zoom_range=0.15,horizontal_flip=True,fill_mode='nearest')VALIDATION_DIR = "/content/FIRE-SMOKE-DATASET/Test"validation_datagen = ImageDataGenerator(rescale = 1./255)train_generator = training_datagen.flow_from_directory(TRAINING_DIR,target_size=(224,224),shuffle = True,class_mode='categorical',batch_size = 128)validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,target_size=(224,224),class_mode='categorical',shuffle = True,batch_size= 14)
  
    
  
  
  

  
    
  
  
  

   
   
     
   
   
   
from tensorflow.keras.applications.inception_v3 import InceptionV3from tensorflow.keras.preprocessing import imagefrom tensorflow.keras.models import Modelfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Input, Dropoutinput_tensor = Input(shape=(224, 224, 3))base_model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=False)x = base_model.outputx = GlobalAveragePooling2D()(x)x = Dense(2048, activation='relu')(x)x = Dropout(0.25)(x)x = Dense(1024, activation='relu')(x)x = Dropout(0.2)(x)predictions = Dense(2, activation='softmax')(x)model = Model(inputs=base_model.input, outputs=predictions)for layer in base_model.layers:  layer.trainable = Falsemodel.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])history = model.fit(train_generator,steps_per_epoch = 14,epochs = 20,validation_data = validation_generator,validation_steps = 14)
  
    
  
  
  

  
    
  
  
  

   
   
     
   
   
   
#To train the top 2 inception blocks, freeze the first 249 layers and unfreeze the rest.for layer in model.layers[:249]:  layer.trainable = Falsefor layer in model.layers[249:]:  layer.trainable = True#Recompile the model for these modifications to take effectfrom tensorflow.keras.optimizers import SGDmodel.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['acc'])history = model.fit(train_generator,steps_per_epoch = 14,epochs = 10,validation_data = validation_generator,validation_steps = 14)
  
    
  
  
  

 

  
    
  
  
  

   
   
     
   
   
   
import cv2import numpy as npfrom PIL import Imageimport tensorflow as tffrom keras.preprocessing import image#Load the saved modelmodel = tf.keras.models.load_model('InceptionV3.h5')video = cv2.VideoCapture(0)while True:        _, frame = video.read()#Convert the captured frame into RGB        im = Image.fromarray(frame, 'RGB')#Resizing into 224x224 because we trained the model with this image size.        im = im.resize((224,224))        img_array = image.img_to_array(im)        img_array = np.expand_dims(img_array, axis=0) / 255        probabilities = model.predict(img_array)[0]        #Calling the predict method on model to predict 'fire' on the image        prediction = np.argmax(probabilities)        #if prediction is 0, which means there is fire in the frame.        if prediction == 0:                frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)                print(probabilities[prediction])cv2.imshow("Capturing", frame)        key=cv2.waitKey(1)        if key == ord('q'):                breakvideo.release()cv2.destroyAllWindows()
  
    
  
  
  

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