基于深度学习YOLO26神经网络实现验证码检测和识别其能识别检测出1种验证码检测names: [captcha]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 licensefrompathlibimportPathfromultralytics.engine.modelimportModelfromultralytics.modelsimportyolofromultralytics.nn.tasksimportClassificationModel, DetectionModel, OBBModel, PoseModel, SegmentationModel, WorldModelfromultralytics.utilsimportROOT, yaml_loadclassYOLO(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-worldinpath.stemandpath.suffixin{.pt,.yaml,.yml}:# if YOLOWorld PyTorch modelnew_instance YOLOWorld(path, verboseverbose) self.__class__ type(new_instance) self.__dict__ new_instance.__dict__else:# Continue with default YOLO initializationsuper().__init__(modelmodel, tasktask, verboseverbose)propertydeftask_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, }, }classYOLOWorld(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 namesifnothasattr(self.model,names): self.model.names yaml_load(ROOT /cfg/datasets/coco8.yaml).get(names)propertydeftask_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, } }defset_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 givenbackground ifbackgroundinclasses: classes.remove(background) self.model.names classes# Reset method class names# self.predictor None # reset predictor otherwise old names remainifself.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/BV1rzTT65EZu/​​