基于YOLOv5模型的火焰识别系统

大家好，YOLOv5模型能够快速准确地检测到火灾火焰，在火灾初期甚至是刚刚出现火苗时就发出警报。这为及时采取灭火措施争取了宝贵的时间，极大地降低了火灾造成的损失。系统可以对特定区域进行持续实时监测，无论白天还是夜晚，都能及时察觉火灾的发生。相比传统的人工巡检或基于传感器的检测方法，具有更高的时效性和可靠性。本文将介绍基YOLOv5模型的火灾火焰检测系统，涵盖准备工作、模型训练等方面。

1.准备工作

在PC机上配置环境，即正常按照requirements安装依赖包，根据实际情况可以自身爬取图片。通过网上的资料下载相关数据集，大部分数据集是无标注的数据集。如下载的数据集无标注，那么使用lableImg进行标注，且将标注文件的保存格式设置为PascalVOC的类型，即xml格式的label文件，而后通过脚本将标签格式转换为.txt文件，并在文件上添加类别信息和对数据进行归一化。脚本脚本代码如下：

import os
import xml.etree.ElementTree as ET
from decimal import Decimaldirpath = '/home/jiu/data_change/label_0'  # 原来存放xml文件的目录
newdir = '/home/jiu/data_change/labels'  # 修改label后形成的txt目录if not os.path.exists(newdir):os.makedirs(newdir)for fp in os.listdir(dirpath):root = ET.parse(os.path.join(dirpath, fp)).getroot()xmin, ymin, xmax, ymax = 0, 0, 0, 0sz = root.find('size')width = float(sz[0].text)height = float(sz[1].text)filename = root.find('filename').textprint(fp)with open(os.path.join(newdir, fp.split('.')[0] + '.txt'), 'a+') as f:for child in root.findall('object'):  # 找到图片中的所有框sub = child.find('bndbox')  # 找到框的标注值并进行读取sub_label = child.find('name')xmin = float(sub[0].text)ymin = float(sub[1].text)xmax = float(sub[2].text)ymax = float(sub[3].text)try:  # 转换成yolov的标签格式，需要归一化到（0-1）的范围内x_center = Decimal(str(round(float((xmin + xmax) / (2 * width)),6))).quantize(Decimal('0.000000'))y_center = Decimal(str(round(float((ymin + ymax) / (2 * height)),6))).quantize(Decimal('0.000000'))w = Decimal(str(round(float((xmax - xmin) / width),6))).quantize(Decimal('0.000000'))h = Decimal(str(round(float((ymax - ymin) / height),6))).quantize(Decimal('0.000000'))print(str(x_center) + ' ' + str(y_center)+ ' '+str(w)+ ' '+str(h))#读取需要的标签if sub_label.text == 'fire':f.write(' '.join([str(0), str(x_center), str(y_center), str(w), str(h) + '\n']))except ZeroDivisionError:print(filename, '的 width有问题')

​ 此处提供本人所使用的火焰数据集，该数据集一共2059张带火焰的图片，并将其分为训练集和测试集，其中训练集1442张，测试集617张；同样的将label也分为训练集和测试集，其图片和其label相对应。 

txt标签：

2.项目实现

对配置文件修改，新建一个.yaml文件，在其中添加(根据实际情况修改文件路径)：

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: D:\a\fire_yolo_format\images\train
val: D:\a\fire_yolo_format\images\val# number of classes
nc: 2# class names
names: ['fire', 'nofire']

在train.py的parse_opt()函数中，修改’–weights’、‘–data’、'–imgsz’等配置，其如下所示：

def parse_opt(known=False):parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default=ROOT / 'pretrained/yolov5s.pt', help='initial weights path')parser.add_argument('--cfg', type=str, default=ROOT / 'models/yolov5s.yaml', help='model.yaml path')parser.add_argument('--data', type=str, default=ROOT / 'data/data.yaml', help='dataset.yaml path')parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')parser.add_argument('--epochs', type=int, default=300)parser.add_argument('--batch-size', type=int, default=4, help='total batch size for all GPUs, -1 for autobatch')parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')parser.add_argument('--rect', action='store_true', help='rectangular training')parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')parser.add_argument('--noval', action='store_true', help='only validate final epoch')parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')# parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')parser.add_argument('--multi-scale', default=True, help='vary img-size +/- 50%%')parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')parser.add_argument('--workers', type=int, default=0, help='max dataloader workers (per RANK in DDP mode)')parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--quad', action='store_true', help='quad dataloader')parser.add_argument('--linear-lr', action='store_true', help='linear LR')parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')# Weights & Biases argumentsparser.add_argument('--entity', default=None, help='W&B: Entity')parser.add_argument('--upload_dataset', action='store_true', help='W&B: Upload dataset as artifact table')parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')opt = parser.parse_known_args()[0] if known else parser.parse_args()return opt

 ’–weights’：添加yolov5的预训练权重文件，此处使用的是yolov5s.pt。如使用其他预训练权重文件，则在val.py中也应当相应修改

‘–data’：数据集的配置文件，即上面定义的.yaml文件

‘–imgsz’：输入图片的大小

在detect.py的parse_opt()函数中，同样修改’–weights’、‘–source’、'–imgsz’等配置，其如下所示：

def parse_opt():parser = argparse.ArgumentParser()parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--view-img', action='store_true', help='show results')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')parser.add_argument('--nosave', action='store_true', help='do not save images/videos')parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')parser.add_argument('--augment', action='store_true', help='augmented inference')parser.add_argument('--visualize', action='store_true', help='visualize features')parser.add_argument('--update', action='store_true', help='update all models')parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')parser.add_argument('--name', default='exp', help='save results to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')opt = parser.parse_args()opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expandprint_args(FILE.stem, opt)return opt

即’–weights’：添加训练好的权重文件

‘–source’：测试图片的路径或测试视频的路径

‘–imgsz’：输入图片的大小

3.训练与测试

如在pycharm中运行，可直接运行train.py文件；如使用终端运行，则运行指令为：

python train.py --img 320 --batch 16 --epoch 300 --data data/coco128.yaml --cfg models/yolov5s.yaml --weights weights/yolov5s.pt --device '0'

图片测试：

# -*- coding: UTF-8 -*-
import time
import cv2
import torch
import copy
from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import check_img_size, non_max_suppression, scale_coords, xyxy2xywhdef load_model(weights, device):model = attempt_load(weights, map_location=device)  # load FP32 modelreturn modeldef show_results(img, xywh, conf, class_num):h, w, c = img.shapelabels = ['fire']tl = 1 or round(0.002 * (h + w) / 2) + 1  # line/font thicknessx1 = int(xywh[0] * w - 0.5 * xywh[2] * w)y1 = int(xywh[1] * h - 0.5 * xywh[3] * h)x2 = int(xywh[0] * w + 0.5 * xywh[2] * w)y2 = int(xywh[1] * h + 0.5 * xywh[3] * h)cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), thickness=tl, lineType=cv2.LINE_AA)tf = max(tl - 1, 1)  # font thicknesslabel = str(labels[int(class_num)]) + ': ' + str(conf)[:5]cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [0, 0, 255], thickness=tf, lineType=cv2.LINE_AA)return imgdef detect_one(model, image_path, device):# Load modelimg_size = 320conf_thres = 0.3iou_thres = 0.2orgimg = cv2.imread(image_path)  # BGR# orgimg = image_pathimg0 = copy.deepcopy(orgimg)assert orgimg is not None, 'Image Not Found ' + image_pathh0, w0 = orgimg.shape[:2]  # orig hwr = img_size / max(h0, w0)  # resize image to img_sizeif r != 1:  # always resize down, only resize up if training with augmentationinterp = cv2.INTER_AREA if r < 1  else cv2.INTER_LINEARimg0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)imgsz = check_img_size(img_size, s=model.stride.max())  # check img_sizeimg = letterbox(img0, new_shape=imgsz)[0]# Convertimg = img[:, :, ::-1].transpose(2, 0, 1).copy()  # BGR to RGB, to 3x416x416# Run inferencet0 = time.time()img = torch.from_numpy(img).to(device)img = img.float()  # uint8 to fp16/32img /= 255.0  # 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:img = img.unsqueeze(0)# Inferencepred = model(img)[0]# Apply NMSpred = non_max_suppression(pred, conf_thres, iou_thres)print('pred: ', pred)print('img.shape: ', img.shape)print('orgimg.shape: ', orgimg.shape)# Process detectionsfor i, det in enumerate(pred):  # detections per imagegn = torch.tensor(orgimg.shape)[[1, 0, 1, 0]].to(device)  # normalization gain whwhif len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classfor j in range(det.size()[0]):xywh = (xyxy2xywh(torch.tensor(det[j, :4]).view(1, 4)) / gn).view(-1).tolist()conf = det[j, 4].cpu().numpy()class_num = det[j, 4].cpu().numpy()orgimg = show_results(orgimg, xywh, conf, class_num)# Stream resultsprint(f'Done. ({time.time() - t0:.3f}s)')cv2.imshow('orgimg', orgimg)cv2.imwrite('filename.jpg',orgimg)if cv2.waitKey(0) == ord('q'):  # q to quitraise StopIterationif __name__ == '__main__':device = torch.device("cuda" if torch.cuda.is_available() else "cpu")weights = '/home/jiu/project/fire_detect/runs/train/exp/weights/best.pt'model = load_model(weights, device)# using imagesimage_path = '/home/jiu/project/fire_detect/test_images/1.jpg'detect_one(model, image_path, device)print('over')

运行结果：

也可直接使用detect.py进行测试，并得到效果图。

摄像头测试：

# -*- coding: UTF-8 -*-
import time
import cv2
import torch
import copy
from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import check_img_size, non_max_suppression, scale_coords, xyxy2xywhdef load_model(weights, device):model = attempt_load(weights, map_location=device)  # load FP32 modelreturn modeldef show_results(img, xywh, conf, class_num):h, w, c = img.shapelabels = ['fire']tl = 1 or round(0.002 * (h + w) / 2) + 1  # line/font thicknessx1 = int(xywh[0] * w - 0.5 * xywh[2] * w)y1 = int(xywh[1] * h - 0.5 * xywh[3] * h)x2 = int(xywh[0] * w + 0.5 * xywh[2] * w)y2 = int(xywh[1] * h + 0.5 * xywh[3] * h)cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), thickness=tl, lineType=cv2.LINE_AA)tf = max(tl - 1, 1)  # font thicknesslabel = str(labels[int(class_num)]) + ': ' + str(conf)[:5]cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [0, 0, 255], thickness=tf, lineType=cv2.LINE_AA)return imgdef detect_one(model, image_path, device):# Load modelimg_size = 320conf_thres = 0.3iou_thres = 0.2orgimg = image_pathimg0 = copy.deepcopy(orgimg)assert orgimg is not None, 'Image Not Found ' + image_pathh0, w0 = orgimg.shape[:2]  # orig hwr = img_size / max(h0, w0)  # resize image to img_sizeif r != 1:  # always resize down, only resize up if training with augmentationinterp = cv2.INTER_AREA if r < 1  else cv2.INTER_LINEARimg0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)imgsz = check_img_size(img_size, s=model.stride.max())  # check img_sizeimg = letterbox(img0, new_shape=imgsz)[0]# Convertimg = img[:, :, ::-1].transpose(2, 0, 1).copy()  # BGR to RGB, to 3x416x416# Run inferencet0 = time.time()img = torch.from_numpy(img).to(device)img = img.float()  # uint8 to fp16/32img /= 255.0  # 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:img = img.unsqueeze(0)# Inferencepred = model(img)[0]# Apply NMSpred = non_max_suppression(pred, conf_thres, iou_thres)print('pred: ', pred)print('img.shape: ', img.shape)print('orgimg.shape: ', orgimg.shape)# Process detectionsfor i, det in enumerate(pred):  # detections per imagegn = torch.tensor(orgimg.shape)[[1, 0, 1, 0]].to(device)  # normalization gain whwhif len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classfor j in range(det.size()[0]):xywh = (xyxy2xywh(torch.tensor(det[j, :4]).view(1, 4)) / gn).view(-1).tolist()conf = det[j, 4].cpu().numpy()class_num = det[j, 4].cpu().numpy()orgimg = show_results(orgimg, xywh, conf, class_num)# Stream resultsprint(f'Done. ({time.time() - t0:.3f}s)')return orgimgif __name__ == '__main__':device = torch.device("cuda" if torch.cuda.is_available() else "cpu")weights = '/home/jiu/project/fire_detect/runs/train/exp/weights/best.pt'model = load_model(weights, device)# using cameracap = cv2.VideoCapture(0)while cap.isOpened():_, frame = cap.read()frame = detect_one(model, frame, device)cv2.imshow("img", frame)cv2.waitKey(1)print('over')