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

author is leilei
fire-detection qq群: 980489677
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new open-source dataset

工作服反光衣数据集: 可用于施工区域or危险区域等指定区域检测: reflective-clothes-detect-dataset

Some details

En: Open source again smoke-fire detection data 10827 sheets (including 2059 labels):
Ch: 再次开源烟雾-火灾检测数据10827张(含2059个标注),仍然需要下载 latest-fire-dataset合并:

BaiDuYunPan_Download 提取码->(hhwq)
latest-fire-dataset (with xml annotations 2059) download: BaiDuYunPan 提取码->(3q4r) GoogleDrive

fire-yolov4-weights download: BaiDuYunPan 提取码->(w3ip)
darknet-yolov4-install-tutorial
yolov4.conv.137 -> GoogleDriver download: yolov4.conv.137
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。

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)?

installed darknet-yolov4, and put darknet_API.py into ./darknet
put cfg into ./darknet
download fire-yolov4's weight, and put it in backup_fire folder

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')

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)?

Convert VOC format data to YOLO format data
Configure file information such as cfg

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！！！

CrystalSixone/fire-detect-yolov4