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基于深度学习YOLO26神经网络实现煤矿安全帽检测和识别其能识别检测出1种煤矿安全帽检测names: [Helmet]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, modelyolo11n.pt, taskNone, verboseFalse): 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, verboseverbose) self.__class__ type(new_instance) self.__dict__ new_instance.__dict__ else: # Continue with default YOLO initialization super().__init__(modelmodel, tasktask, verboseverbose) 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, modelyolov8s-world.pt, verboseFalse) - 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__(modelmodel, taskdetect, verboseverbose) # 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 its 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预测代码#coding:utf-8 from ultralytics import YOLO import cv2 # 所需加载的模型目录 path models/best.pt # 需要检测的图片地址 img_path TestFiles/000353.jpg # 加载预训练模型 # conf 0.25 object confidence threshold for detection # iou 0.7 intersection over union (IoU) threshold for NMS model YOLO(path, taskdetect) results model.predict(img_path, iou0.5) # 检测图片 res results[0].plot() cv2.imshow(YOLO26 Detection, res) cv2.waitKey(0)第六步整个工程的内容包含数据集、训练代码和预测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/BV1P1Kz6qEjA/
基于深度学习YOLO26神经网络实现煤矿安全帽检测和识别其能识别检测出1种煤矿安全帽检测names: [Helmet]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, modelyolo11n.pt, taskNone, verboseFalse): 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, verboseverbose) self.__class__ type(new_instance) self.__dict__ new_instance.__dict__ else: # Continue with default YOLO initialization super().__init__(modelmodel, tasktask, verboseverbose) 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, modelyolov8s-world.pt, verboseFalse) - 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__(modelmodel, taskdetect, verboseverbose) # 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 its 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预测代码#coding:utf-8 from ultralytics import YOLO import cv2 # 所需加载的模型目录 path models/best.pt # 需要检测的图片地址 img_path TestFiles/000353.jpg # 加载预训练模型 # conf 0.25 object confidence threshold for detection # iou 0.7 intersection over union (IoU) threshold for NMS model YOLO(path, taskdetect) results model.predict(img_path, iou0.5) # 检测图片 res results[0].plot() cv2.imshow(YOLO26 Detection, res) cv2.waitKey(0)第六步整个工程的内容包含数据集、训练代码和预测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/BV1P1Kz6qEjA/
