fire-detect-yolov4-5 and fire-detect-dataset

• author is leilei
• fire-detection qq群: 980489677
• 如果此项目对您有所帮助，请给个star，您的star是对我的鼓励！

Add important information

• Open source again smoke-fire detection data 10827 sheets (including 2059 labels):
• 再次开源烟雾-火灾检测数据10827张(含2059个标注):
• BaiDuYunPan_Download 提取码->(hhwq)
• 仍然需要下载 latest-fire-dataset 合并
• Darknet has updated part of the code, so my code is not suitable for the latest darknet, please refer to the current darknet_images.py file.
• Darknet已更新了部分代码，因此我的代码不适用于最新的Darknet，请参考当前的darknet_images.py文件。

Some details

• darknet-yolov4-install-tutorial
• latest-fire-dataset (with xml annotations 2059) download: BaiDuYunPan 提取码->(3q4r) GoogleDrive
• fire-yolov4-weights download: BaiDuYunPan 提取码->(w3ip)
• yolov4.conv.137 -> GoogleDriver download: yolov4.conv.137

How to use dataset?

• We annotate the fire-detection-dataset as Pascal VOC format:

  --VOC2020
      --Annotations (xml_num: 2059)
      --ImageSets(Main)
      --JPEGImages (image_num: 2059)
      
      --label_name: fire
  

• Unzip **.tar file command

  请不要再问我如何解压！
  tar -xzvf ***.tar  (win or linux: Git Bash)
  or 
  7zip (win: 7zip; 360zip need 2 time unzip)
  

• If you want to convert VOC to YOLO format:

  Call darknet-yolov4's scripts voc_label.py
  

• Fire scene:

  vehicle-fire、grassland-fire、forest-fire、building-fire、Big and small fire、Day and night fire;
  

• Crawl fire-smoke images

  * crawl baidu images: test_baidu.py
  * crawl google images: test_google.py
  

How to use this code (test)?

0. installed darknet-yolov4, and put darknet_API.py into ./darknet
1. put cfg into ./darknet
2. download fire-yolov4's weight, and put it in backup_fire folder
3. Call the darknet_API main function:

   from darknet_API import Detect
   detect = Detect(metaPath=r'./cfg/fire.data', configPath=r'./cfg/yolov4-fire.cfg',\
                   weightPath=r'./backup_fire/yolov4-fire_best.weights',\
                   namesPath=r'./cfg/fire.names')
   image = cv2.imread(r'/home/Datasets/20200714085948.jpg', -1)
   draw_img = detect.predict_image(image, save_path='./pred.jpg')
   

4. The latest version of darknet has modified darknet.py and can directly perform image detection based on it.

• Note:
  • This project should be placed in the ./darknet folder;
  • Fire generally coexists with smoke, but we only marked fire;
  • In addition, it is easy to confuse the negative sample of the fire and the light;

How to train yolov4 in darknet (train)?

0. Convert VOC format data to YOLO format data
1. Configure file information such as cfg
2. Call the darknet command:

   ./darknet detector train cfg/fire.data cfg/yolov4-fire.cfg yolov4.conv.137 -gpus 0 -map -dont_show
   

demo

• ./result: fire-detect demos

• ./xml_lab: fire-detection image annotations

Cite

• train_data contain 1-2-3-4:
• train_data1: https://blog.csdn.net/LEILEI18A/article/details/107334474
• train_data2: https://bitbucket.org/gbdi/bowfire-dataset/downloads/
• train_data3: https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/fire-dataset.zip
• train_data4: https://github.com/cair/Fire-Detection-Image-Dataset/blob/master/Fire%20images.rar
• fire-demo-dataset: http://signal.ee.bilkent.edu.tr/VisiFire/Demo/SampleClips.html
• google云盘下载链接由qq群中小伙伴提供

Reputation:

• This data set contains 2 parts:
• (1) Images crawled by myself, marked by myself
• (2) The data that others open source, some have annotations, some have no annotations (I re-annotate it)
• 本数据仅学术探索！！！

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