Feb 15 2022

火灾烟雾检测

阿里云代理

分类：linux图文教程阅读次数

已被围观 986 次

背景介绍

火灾初期通常会产生大量烟雾，在很大程度上降低了火焰检测的有效性，通过计算机视觉的方法对烟雾进行监控，可以实现早期火灾预警。计算能力的提高、存储设备的发展，使得传统视觉技术中存在的问题逐渐得到改善或解决。

项目概述

下面我们采用paddleDetection套件为大家实现一个火灾烟雾检测。对于数据的标注，我们主要标注了火场的烟和火。对于检测属于一个二分类的问题。

开始项目

Step1:安装环境

使用时候请先务必检查一下自己的环境，本文采用paddle-gpu最新稳定版本，其他版本操作基本类似。如果环境满足，则可以直接跳过环境安装.
如果大家是CPU环境，则安装paddle相应的版本即可

!python -m pip install paddlepaddle-gpu==2.2.0.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple

Looking in links: https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

Requirement already satisfied: paddlepaddle-gpu==2.2.0.post101 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.0.post101)

Requirement already satisfied: astor in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (0.8.1)

Requirement already satisfied: numpy>=1.13; python_version >= "3.5" and platform_system != "Windows" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (1.20.3)

Requirement already satisfied: protobuf>=3.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (3.14.0)

Requirement already satisfied: requests>=2.20.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (2.22.0)

Requirement already satisfied: decorator in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (4.4.2)

Requirement already satisfied: Pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (7.1.2)

Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlepaddle-gpu==2.2.0.post101) (1.15.0)

Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle-gpu==2.2.0.post101) (2.8)

Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle-gpu==2.2.0.post101) (1.25.6)

Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle-gpu==2.2.0.post101) (2019.9.11)

Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.20.0->paddlepaddle-gpu==2.2.0.post101) (3.0.4)

Step2:安装PaddleDetection

下载PaddleDetection并安装至Notebook
安装PaddleDetection的相关依赖

进入工作目录，下载相关代码。如果大家从github上clone代码较慢，可以切换至gitee上下载。

!cd   /home/aistudio/work&&git clone https://gitee.com/PaddlePaddle/PaddleDetection.git -b develop

Cloning into 'PaddleDetection'...

remote: Enumerating objects: 760, done.

remote: Counting objects: 100% (760/760), done.

remote: Compressing objects: 100% (418/418), done.

remote: Total 20290 (delta 493), reused 508 (delta 342), pack-reused 19530

Receiving objects: 100% (20290/20290), 201.01 MiB | 29.73 MiB/s, done.

Resolving deltas: 100% (15042/15042), done.

Checking connectivity... done.

%cd   /home/aistudio/work/PaddleDetection/

/home/aistudio/work/PaddleDetection

! pip install -r requirements.txt    #安装项目必要依赖

Step3:上传数据集并解压

本案例使用数据集包含MIT协议互联网下载图片和MIT协议公开数据集，共有6675张图片，分别检测烟雾和火灾，示例图片如下图所示：

数据集图片格式是VOC数据格式，VOC数据是每个图像文件对应一个同名的xml文件，xml文件内包含对应图片的基本信息，比如文件名、来源、图像尺寸以及图像中包含的物体区域信息和类别信息等。

本文采用的数据集来源于开源数据集，感兴趣的也可以本地下载进行训练。

%cd /home/aistudio/data/data118365/

/home/aistudio/data/data118365

解压data目录下的数据集

! unzip fire_smoke.zip

inflating: __MACOSX/fire_smoke/annotations/._smoke_01167.xml 267c8-0c0a-4244-8fcc-d2a7e9774387.xml

Step4:配置训练文件

PP-YOLOv2：

相较20年发布的PP-YOLO，PP-YOLOv2版本在COCO 2017 test-dev上的精度提升了3.6个百分点，由45.9%提升到了49.5%；在640*640的输入尺寸下，FPS达到68.9FPS。主要改进点：

Path Aggregation Network
Mish Activation Function
Larger Input Size
IoU Aware Branch

配置ppyolov2_r50vd_dcn_voc.yml文件

路径 /home/aistudio/work/PaddleDetection/configs/ppyolo/ppyolov2r50vddcnvoc.yml 其中置文件中的epoch可以根据实际情况调整，如果原文件的设置，训练时间一般较长。

另外大家可以选用较小的网络比如mobilenetv3-small等。本文配置为了演示，将下面的总epoch数目设置为10，训练时长大概3个小时左右。如果大家想要提高检测精度可以根据实际情况增加epoch数目。

BASE_: [  '../datasets/voc.yml',  '../runtime.yml',  './_base_/ppyolov2_r50vd_dcn.yml',  './_base_/optimizer_365e.yml',  './_base_/ppyolov2_reader.yml', ] snapshot_epoch: 2    # 保存模型epoch间隔 weights: output/ppyolov2_r50vd_dcn_voc/model_final    # 模型权重路径配置 TrainReader:  mixup_epoch: 6   #数据增强的epoch数目，根据实际情况调整  batch_size: 12   # 批次数目也可以适当进行调整 EvalReader:  collate_batch: false # 表示reader是否对gt进行组batch的操作，在rcnn系列算法中设置为false，得到的gt格式为list[Tensor] epoch: 10  #总epoch数目，上面的eponch不能超过此处设置数目 LearningRate:  base_lr: 0.00333  schedulers:  - !PiecewiseDecay  gamma: 0.1  milestones:  - 466  - 516  - !LinearWarmup  start_factor: 0.  steps: 4000 OptimizerBuilder:  optimizer:  momentum: 0.9  type: Momentum  regularizer:  factor: 0.0005  type: L2

配置voc.yml文件

配置数据读取路径 /home/aistudio/work/PaddleDetection/configs/datasets/voc.yml 比如：

metric: VOC map_type: 11point num_classes: 2 TrainDataset:  !VOCDataSet  dataset_dir: /home/aistudio/data/data118365/fire_smoke/   # 数据集所在的文件夹  anno_path: train_list.txt    # 训练集文件名  label_list: labels.txt   # 标签文件名  data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult'] EvalDataset:  !VOCDataSet  dataset_dir: /home/aistudio/data/data118365/fire_smoke/  anno_path: val_list.txt   # 评估数据集文件名  label_list: labels.txt  data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']

Step5:模型训练

进入相应路径
启动单卡训练
选择训练配置并执行训练命令

config_path = '/home/aistudio/work/PaddleDetection/configs/ppyolo/ppyolov2_r50vd_dcn_voc.yml'  # 配置文件设置

!export CUDA_VISIBLE_DEVICES=0   # 配置单卡，如果大家环境有多卡可以配置为 0,1,2,3

Tips: 上述两个命令也可以通过脚本来直接执行，配置完成后开始训练

!python /home/aistudio/work/PaddleDetection/tools/train.py -c $config_path   # 启动训练

由于输出内容较长本文仅截取最后结果作为展示：

Step6:模型评估

设置模型路径

weight_path = '/home/aistudio/data/data118365/output/ppyolov2_r50vd_dcn_voc/model_final.pdparams'

Step7:模型预测

通过-c选项指定待分析模型的配置文件的相对路径

通过-o weights指定模型的权重，可以指定url或本地文件系统的路径

!python /home/aistudio/work/PaddleDetection/tools/eval.py -c $config_path -o weights=$weight_path

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:130: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

if data.dtype == np.object:

Warning: import ppdet from source directory without installing, run 'python setup.py install' to install ppdet firstly

W1128 19:19:02.993319 5384 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1

W1128 19:19:02.998888 5384 device_context.cc:465] device: 0, cuDNN Version: 7.6.

[11/28 19:19:07] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/data/data118365/output/ppyolov2_r50vd_dcn_voc/model_final.pdparams

[11/28 19:19:08] ppdet.engine INFO: Eval iter: 0

[11/28 19:19:32] ppdet.metrics.metrics INFO: Accumulating evaluatation results...

[11/28 19:19:32] ppdet.metrics.metrics INFO: mAP(0.50, 11point) = 14.56%

[11/28 19:19:32] ppdet.engine INFO: Total sample number: 667, averge FPS: 26.75302856086791

设置预测图片路径

可以自定义图片路径

由于模型评估后精度不高（当前只是简单训练，训练epoch较低），这里预测模型设置置信度在0.1以上的火灾烟雾检测

img_path = '/home/aistudio/work/images/fire_work.png'   # 待预测图片路径 thresh = 0.2  # 设置预测的阈值，可以根据实际情况进行调整，设置过大，可能预测结果为空，即图片没有任何的检测结果

模型预测

!python /home/aistudio/work/PaddleDetection/tools/infer.py -c $config_path -o weights=$weight_path -o use_gpu=True --draw_threshold=$thresh --infer_img=$img_path

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:130: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

if data.dtype == np.object:

Warning: import ppdet from source directory without installing, run 'python setup.py install' to install ppdet firstly

W1128 19:21:40.539165 5722 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1

W1128 19:21:40.544663 5722 device_context.cc:465] device: 0, cuDNN Version: 7.6.

[11/28 19:21:44] ppdet.utils.checkpoint INFO: Finish loading model weights: output/ppyolov2_r50vd_dcn_voc/model_final.pdparams

[11/28 19:21:44] ppdet.engine INFO: Detection bbox results save in output/fire_work.png

最后可以打开预测结果，类似于这样