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网站的推广方式有哪些,福建省城乡建设网站,wordpress图文发布,好的响应式网站有哪些基于深度学习YOLOv12神经网络实现摔倒检测和识别#xff0c;其能识别检测出2种摔倒检测#xff1a;names: [Fall Detected, NoFall Detected] 具体图片见如下#xff1a; 第一步#xff1a;YOLOv12介绍 YOLO12 引入了一种以注意力为中心的架构#xff0c;它脱离了之前 Y…基于深度学习YOLOv12神经网络实现摔倒检测和识别其能识别检测出2种摔倒检测names: [Fall Detected, NoFall Detected]具体图片见如下第一步YOLOv12介绍YOLO12 引入了一种以注意力为中心的架构它脱离了之前 YOLO 模型中使用的传统 CNN 方法但保留了许多应用所必需的实时推理速度。该模型通过在注意力机制和整体网络架构方面的创新方法实现了最先进的目标检测精度同时保持了实时性能。主要功能区域注意力机制: 一种新的自注意力方法可以有效地处理大型感受野。它将特征图分成l个大小相等的区域默认为 4 个水平或垂直避免复杂的运算并保持较大的有效感受野。与标准自注意力相比这大大降低了计算成本。残差高效层聚合网络R-ELAN一种基于 ELAN 的改进的特征聚合模块旨在解决优化挑战尤其是在更大规模的以注意力为中心的模型中。R-ELAN 引入具有缩放的块级残差连接类似于层缩放。一种重新设计的特征聚合方法创建了一个类似瓶颈的结构。优化的注意力机制架构YOLO12 精简了标准注意力机制以提高效率并与 YOLO 框架兼容。这包括使用 FlashAttention 来最大限度地减少内存访问开销。移除位置编码以获得更简洁、更快速的模型。调整 MLP 比率从典型的 4 调整到 1.2 或 2以更好地平衡注意力和前馈层之间的计算。减少堆叠块的深度以改进优化。利用卷积运算在适当的情况下以提高其计算效率。在注意力机制中添加一个7x7可分离卷积“位置感知器”以隐式地编码位置信息。全面的任务支持: YOLO12 支持一系列核心计算机视觉任务目标检测、实例分割、图像分类、姿势估计和旋转框检测 (OBB)。增强的效率: 与许多先前的模型相比以更少的参数实现了更高的准确率从而证明了速度和准确率之间更好的平衡。灵活部署: 专为跨各种平台部署而设计从边缘设备到云基础设施。第二步YOLOv12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(YOLOv12 Detection, res) cv2.waitKey(0)第六步整个工程的内容包含摔倒数据集、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/BV1L3qYByEkm/