【免费】KeywordsFlamesmokedetection,Targetrecognition,YOLOvS,Imag资源-CSDN文库

yolo

火焰识别

需积分: 0 160 浏览量 2024-04-30 11:45:53 上传评论收藏 4.5MB PDF 举报

资源推荐

资源详情

资源评论

Flame smoke detection

algorithm based on YOLOv5

in petrochemical plant

Yueting Yang

Guangdong University of Petrochemical Technology, Maoming, China and

Jilin Institute of Chemical Technology, jilin, China, and

Shaolin Hu, Ye Ke and Runguan Zhou

Automation School, Guangdong University of Petrochemical Technology,

Maoming, China

Abstract

Purpose – Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life

safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke

detection under complex factory background.

Design/methodology/approach – This paper presents a flame smoke detection algorithm based on

YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection

in target detection and improve the model detection performance. The improved activation function avoids

gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement

technology is used to enhance the ability of the network to extract features and improve the accuracy of the

model for small target detection.

Findings – Based on the actual situation of flame smoke, the loss function and activation function of YOLOv5

model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with

generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The

accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on

flame smoke. The improved network model is superior to the existing methods in running time and accuracy.

Originality/value – Aiming at the actual particularity of flame smoke detection, an improved flame smoke

detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by

improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid

over-fitting of the network. This method is helpful to improve the problems of missed detection and false

detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle

running recognition.

Keywords Flame smoke detection, Target recognition, YOLOv5, Image detection, Deep learning

Paper type Research paper

1. Introduction

The production safety of the factory has always been a problem that cannot be ignored. Taking

petrochemical plants as an example, due to the dense production workshops and the existence of

a large number of flammable and explosive dangerous goods, once a fire occurs, it is easy to

induce catastrophic consequences, cause environmental pollution and seriously threaten

production safety and personnel life and property safety. Therefore, timely detection and early

warning control of early fires is a realistic demand for safe production. International and domestic

attention has been paid to the flame smoke detection and alarm technology in the factory area.

Early fire detection is mainly achieved through smoke sensors and temperature sensors. For

example, smoke sensors complete fire prevention by detecting smoke concentration. This method

has a good performance in indoor or some small places. However, in a complex environment, due

to the influence of factors such as airflow environment and thermal barrier effect, coupled with

IJICC

16,3

502

This work was supported by National Natural Science Foundation of China (61973094).

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/1756-378X.htm

Received 18 November 2022

Revised 8 December 2022

Accepted 2 January 2023

International Journal of Intelligent

Computing and Cybernetics

Vol. 16 No. 3, 2023

pp. 502-519

1756-378X

DOI 10.1108/IJICC-11-2022-0291

the close detection distance of the sensor and low stability, it is difficult to accurately obtain

on-site real-time signal data information by relyingsolelyonthesensorto detect temperature,

concentration and other indicators. Feng and Yang (2019) designed an automatic fire

detection and alarm and automatic fire extinguishing system, the system to achieve intelligent

detection of fire, automatic alarm and real-time accurate fire extinguishing function.

In recent years, with the continuous development of intelligent image processing technology,

especially the application of various models and methods based on deep learning, v arious

intelligent flame smoke detection methods have been widely studied and applied. Many scholars

at home and abroad have applied deep learning technology to flame smoke detection. Ryu and

Kwak (2021) used HSV color conversion and Harris angle detection in the image preprocessing

step and used convolutional neural network to determine the fire area, reducing the missed

detection rate. Jiang and Bai (2019) proposed a flame detection algorithm based on RetinaNet

model, which realized real-time end-to-end flame recognition and positioning. For forest fire

smoke detection, Rager Julia et al. (2021) proposed to increase STN (spatial transformer network)

and entropy function threshold in convolutional neural network to improve the accuracy of

smoke recognition. In order to recognize smoke, Yu (2010) combined the texture features of smoke

and deep learning. Firstly, the author used gray level co-occurrence matrix to recognize the

texture features of smoke and then classified and recognized the smoke blocks by BP neural

network. Through experiments, it is found that the algorithm has a better effect on smoke

recognition. Gao et al. (2018) used the CNN model to identify the smoke image and then combined

the dynamic texture features of the smoke to determine whether it was smoke. This method

reduces the influence of external illumination and smoke concentration changes on model

recognition. Wang (2020a) proposed a smoke detection algorithm using Faster R-CNN. Firstly, the

smoke is extracted by motion detection of the smoke, and then the Faster R-CNN network is used

to extract and identify the smoke image features. Deep learning method is superior to the

traditional artificial feature extraction (Maditham et al., 2022). CNN model has a strong ability to

extract foreground and background features (Huang, 2021; Rao Kota and Devi Munisamy, 2021),

can be used to extract more abstract and deeper features in flame.

Although the research on target detection based on the above methods has made a major

breakthrough, in the actual target detection, the entire image needs to be convoluted,

requiring a larger field of view to meet the algorithm brief and fast. YOLO is a target detector

that uses features learned by deep convolutional neural networks to detect objects. In recent

years, a large number of studies have used it for intelligent detection of different types of

images ( Zhang et al., 2022a). Kris (2020) performed online image enhancement on the self-built

training set by improving the backbone network of YOLOv3 and used the local feature idea to

classify the flame pre-selected area images to achieve smoke detection and flame detection

simultaneously. Ren et al. (2019) used the improved YOLOv3 network to realize the detection

and recognition of fire. The algorithm improves the recognition accuracy and detection speed

of small target smoke by improving the prediction frame size of K-means clustering

algorithm in YOLOv3. Cao et al. (2021) proposed a YOLOv4 accuracy improvement strategy

based on multi-scale feature maps and made some improvements by improving the feature

extraction network to detect small objects.

Xue et al. (2022) proposed an improved model

based on YOLOv5s to improve the accuracy of small target forest fire detection by using

transfer learning method for the small target size in long-distance forest fire images, which is

difficult to capture effective information. However, the model structure is complex and the

flame detection accuracy is not enough (Sukumaran and Brindha, 2020).

In view of the excellent performance of the YOLOv5 algorithm model with small size and fast

detection speed, and due to the complexity of treating the flame smoke detection problem in the

plant area (Zhang et al., 2022b), this article establishes a plant flame smoke detection algorithm on

the basis of specially improved YOLOv5, which is used to solve the problem of missed detection

and false detection of flame smoke. In this algorithm, the original GIoU _ Loss is replaced by CIoU

Smoke

detection

algorithm

503

_ Loss as the loss function of the bounding box, and the SiLU activation function is utilized to

avoid over-fitting of the network. Finally, the effectiveness and accessibility of the proposed

algorithm are verified by comparing with SSD, YOLOv4-tiny and YOLOv7 algorithms.

Specifically, the main research work of this article consists of four sections: Section 1

briefly introduces the network structure of YOLOv5 and elaborates the optimization process

of object detection algorithm based on YOLOv5; section 2, through the independent

construction of two types of data sets, the hyper parameter settings for model preprocessing;

in section 3, three evaluation indexes are selected to evaluate the ability of the network model,

and the algorithm is compared with SSD and YOLOv4-tiny algorithm in the same data set

and experimental environment. Section 4 summarizes the research content of this article and

analyzes the shortcomings of the research as the next stage of the research task. It is verified

that the algorithm in this article takes into account both accuracy and speed in real-time

monitoring of flame smoke, indicating that it has good practicability.

2. Method

2.1 YOLOv5 network model and algorithm improvement

As a new target detection method, YOLO has the characteristics of fast target detection and

high accuracy (Yu, 2022). Therefore, the YOLOv5 model with excellent accuracy has been

greatly popularized in object detection and recognition. The model is improved on the basis of

YOLOv3 and incorporates some new technologies in the field of target detection in recent

years to improve the detection performance of the model (Hua-wei et al., 2022). YOLOv5 is

implemented using the deep learning framework PyTorch, using additional data sets to

enhance training to achieve efficient and accurate recognition of targets. Compared with the

Darknet framework used by YOLOv1 ∼ YOLOv4, the software and hardware support is

relatively complete and supports deployment on a variety of devices. Therefore, YOLOv5 is

selected as the experimental object to detect flame smoke in the plant.

2.1.1 YOLOv5 network model structure. The structure of YOLOv5 can be divided into four

parts: input, backbone, Backbone, Neck and Prediction (Niu et al., 2022). CSP, Focus and SPP

structures are used in the backbone network. The CSP1 _ X and CSP2 _ X structur es in CSP are

applied to Backbone and Neck respectively. CSP solves the network optimization problem of

repeated gradient information in the backbone of other large-scale convolutional neural network

frameworks, reduces the model size and improves the inference speed and accuracy (Fahad et al.,

2022). Focus is a slicing operation on the feature map, which integrates width and height

information into multiple dimensions to improve reasoning speed. The SPP (Spatial Pyramid

Pooling) module performs a maximum pooling of 5 3 5, 9 3 9, 13 3 13 on the image to extract

features from multiple aspects and then concat slices the width and height of the fused image

(Dou et al., 2021). These structures effectively avoid the problems of image distortion caused by

image region clipping and scaling operations and also solve the problem of repeated feature

extraction of images by convolutional neural networks. The Neck layer uses the FPN þ PAN

structure to obtain high-level semantic feature maps and enhance the positioning information and

transmits the image features to the prediction layer to enhance the recognition ability of the model

(Wang, 2022). NMS performs non-maximal suppression on occluded overlapping targets to

obtain the optimal target frame (see Figure 1).

The YOLOv5 adjusted by parameters can be divided into f our models of d ifferent siz es,

namely v5s, v5m, v5l and v5x (Yan-hua et al., 2022). The ov erall stru cture of t he four model s

is the same, and the difference mainly lies in the depth and width of the model, that is, th e

number of model layers and the number of convolutional layers are different, and the

weight file sizes are 14, 42, 93 and 170 MB, respectively. As the network model continues to

deepen, the model detection accuracy continue s to improve, and it has stronger feature

extraction and feature fusion capabilities. As the model deepens , the volume is also

IJICC

16,3

504

剩余17页未读，继续阅读

评论收藏

内容反馈

羁旅少年

粉丝: 4719
资源: 4

Keywords Flame smoke detection, Target recognition, YOLOvS, Imag

最新资源

Keywords Flame smoke detection, Target recognition, YOLOvS, Imag

2D Object Detection and Recognition

Scene text detection and recognition with advances in deep learning.pdf

Object Detection and Recognition Using Deep Learning in OpenCV [Chapter 1 and 2]

OBJECT DETECTION AND RECOGNITION IN DIGITAL IMAGES

Scene text detection and recognition_ recent advances and future trends.pdf

Object Detection and Recognition in Digital Images Theory and Practice-PPTs

Eigenfaces for Face Detection & Recognition

Computer Vision Detection, Recognition and Reconstruction

Text Detection and Recognition in Imagery

计算机视觉Computer Vision Detection,Recognition and Reconstruction

Automatic target recognition from highly incomplete SAR data

Detection and Recognition of Moving Video Objects Kalman Filtering with DL

Vehicle_Detection_Recognition-matlab

Face Detection and Recognition on Mobile Devices

New Discrimination Features for SAR Automatic Target Recognition

Review of Scene Text Detection and Recognition

survey of neural network technology for automatic target recognition

YOLOv8-deepsort 实现智能车辆目标检测+车辆跟踪+车辆计数

YOLOv8网络结构图，自制visio文件，yolov8.vsds，需要的自取，在原有的基础上直接改就行了

yolov8(2023年8月版本),已经下好yolov8s.pt和yolov8n.pt

Transformer模型实现长期预测并可视化结果（附代码+数据集+原理介绍）

社交平台上经济类话题的文章热度信息，数据是真实的，但不是真实日期

行人跌倒数据集（VOC格式）

YOLOV5 + 双目相机实现三维测距（新版本）

Unet眼底血管图像分割数据集+代码+模型+系统界面+教学视频.zip

全新的SOTA模型YOLOv9

YOLOV5口罩检测数据集+代码+模型 2000张标注好的数据+教学视频.zip

基于YOLOv8-Pose的姿态识别项目，带数据集可直接跑通的源码

pycharm连接autodl服务器（yolov8训练自己的数据集）

最新资源