柑橘病害检测和识别2：基于YOLOv11柑橘病害识别系统(含训练代码和数据集)

基于YOLOv11柑橘病害识别系统其能识别检测出4种病害names {0:blackspot, 1:canker, 2:fresh, 3:grenning}具体图片见如下第一步YOLOv11介绍YOLOv11是由Ultralytics公司开发的新一代目标检测算法它在之前YOLO版本的基础上进行了显著的架构和训练方法改进。以下是YOLOv11的一些详细介绍和创新点增强的特征提取YOLOv11采用了改进的骨干网络和颈部架构增强了特征提取能力以实现更精确的目标检测和复杂任务的性能。优化效率和速度引入了精细的架构设计和优化的训练流程提供了更快的处理速度并在准确性和性能之间保持了最佳平衡。更少参数下的高准确度YOLOv11在COCO数据集上实现了更高的平均精度均值mAP同时比YOLOv8少用了22%的参数使其在不牺牲准确性的情况下具有计算效率。跨环境的适应性YOLOv11可以无缝部署在各种环境中包括边缘设备、云平台和支持NVIDIA GPU的系统确保了最大的灵活性。支持广泛的任务YOLOv11不仅支持目标检测还支持实例分割、图像分类、姿态估计和定向目标检测OBB满足一系列计算机视觉挑战。YOLOv11的网络结构和关键创新点包括C3k2机制这是一种新的卷积机制它在网络的浅层将c3k参数设置为False类似于YOLOv8中的C2f结构。C2PSA机制这是一种在C2机制内部嵌入的多头注意力机制类似于在C2中嵌入了一个PSA金字塔空间注意力机制。深度可分离卷积DWConv在分类检测头中增加了两个DWConv这种卷积操作减少了计算量和参数量提高了模型的效率。自适应锚框机制自动优化不同数据集上的锚框配置提高了检测精度。EIoU损失函数引入了新的EIoUExtended IoU损失函数考虑了预测框与真实框的重叠面积长宽比和中心点偏移提高了预测精度。YOLOv11的训练过程包括数据准备、数据增强、超参数优化和模型训练几个阶段。它使用混合精度训练技术在不降低模型精度的情况下加快了训练速度并减少了显存的占用。在部署方面YOLOv11支持导出为不同的格式如ONNX、TensorRT和CoreML以适应不同的部署平台。它还采用了多种加速技术如半精度浮点数推理FP16、批量推理和硬件加速以提升推理速度。YOLOv11的成功标志着目标检测技术又迈出了重要的一步它为开发者提供了更强大的工具来应对日益复杂的视觉检测任务。第二步YOLOv11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(YOLOv11 Detection, res) cv2.waitKey(0)第六步整个工程的内容包含手势手语数据集、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/BV1fGL66nEMD/​