基于深度学习YOLO26神经网络实现火焰烟雾检测和识别，其能识别检测出2种火焰烟雾检测：names: 'fire','smoke'
具体图片见如下：
第一步：YOLO26介绍
YOLO26采用了端到端无NMS推理，直接生成预测结果，无需非极大值抑制（NMS）后处理。这种设计减少了延迟，简化了集成，并提高了部署效率。此外，YOLO26移除了分布焦点损失（DFL），从而增强了硬件兼容性，特别是在边缘设备上的表现。
模型还引入了ProgLoss 和小目标感知标签分配（STAL） ，显著提升了小目标检测的精度。这对于物联网、机器人技术和航空影像等应用至关重要。同时，YOLO26采用了全新的MuSGD优化器，结合了SGD和Muon优化技术，提供更稳定的训练和更快的收敛速度。
第二步：YOLO26网络结构
第三步：代码展示
# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path
from ultralytics.engine.model import Model
from ultralytics.models import yolo
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, OBBModel, PoseModel, SegmentationModel, WorldModel
from ultralytics.utils import ROOT, yaml_load
class YOLO(Model):
"""YOLO (You Only Look Once) object detection model."""
def __init__(self, model="yolo11n.pt", task=None, verbose=False):
"""Initialize YOLO model, switching to YOLOWorld if model filename contains '-world'."""
path = Path(model)
if "-world" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}: # if YOLOWorld PyTorch model
new_instance = YOLOWorld(path, verbose=verbose)
self.__class__ = type(new_instance)
self.__dict__ = new_instance.__dict__
else:
# Continue with default YOLO initialization
super().__init__(model=model, task=task, verbose=verbose)
@property
def task_map(self):
"""Map head to model, trainer, validator, and predictor classes."""
return {
"classify": {
"model": ClassificationModel,
"trainer": yolo.classify.ClassificationTrainer,
"validator": yolo.classify.ClassificationValidator,
"predictor": yolo.classify.ClassificationPredictor,
},
"detect": {
"model": DetectionModel,
"trainer": yolo.detect.DetectionTrainer,
"validator": yolo.detect.DetectionValidator,
"predictor": yolo.detect.DetectionPredictor,
},
"segment": {
"model": SegmentationModel,
"trainer": yolo.segment.SegmentationTrainer,
"validator": yolo.segment.SegmentationValidator,
"predictor": yolo.segment.SegmentationPredictor,
},
"pose": {
"model": PoseModel,
"trainer": yolo.pose.PoseTrainer,
"validator": yolo.pose.PoseValidator,
"predictor": yolo.pose.PosePredictor,
},
"obb": {
"model": OBBModel,
"trainer": yolo.obb.OBBTrainer,
"validator": yolo.obb.OBBValidator,
"predictor": yolo.obb.OBBPredictor,
},
}
class YOLOWorld(Model):
"""YOLO-World object detection model."""
def __init__(self, model="yolov8s-world.pt", verbose=False) -> None:
"""
Initialize YOLOv8-World model with a pre-trained model file.
Loads a YOLOv8-World model for object detection. If no custom class names are provided, it assigns default
COCO class names.
Args:
model (str | Path): Path to the pre-trained model file. Supports *.pt and *.yaml formats.
verbose (bool): If True, prints additional information during initialization.
"""
super().__init__(model=model, task="detect", verbose=verbose)
# Assign default COCO class names when there are no custom names
if not hasattr(self.model, "names"):
self.model.names = yaml_load(ROOT / "cfg/datasets/coco8.yaml").get("names")
@property
def task_map(self):
"""Map head to model, validator, and predictor classes."""
return {
"detect": {
"model": WorldModel,
"validator": yolo.detect.DetectionValidator,
"predictor": yolo.detect.DetectionPredictor,
"trainer": yolo.world.WorldTrainer,
}
}
def set_classes(self, classes):
"""
Set classes.
Args:
classes (List(str)): A list of categories i.e. ["person"].
"""
self.model.set_classes(classes)
# Remove background if it's given
background = " "
if background in classes:
classes.remove(background)
self.model.names = classes
# Reset method class names
# self.predictor = None # reset predictor otherwise old names remain
if self.predictor:
self.predictor.model.names = classes第四步：统计训练过程的一些指标，相关指标都有
第五步：运行（支持图片、文件夹、摄像头和视频功能）
第六步：整个工程的内容
有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码
项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷
https://www.bilibili.com/video/BV1U9rQBaEYd/
