电力设施及缺陷检测数据集8932张yolo和voc两种标注方式19类标注数量Insulator: 绝缘子 - 3408Broken Insulator Cap: 破损的绝缘子帽 - 2011Insulator Cap: 绝缘子帽 - 22722Cable: 电缆 - 24605Tower (Wooden): 木塔 - 875Transformer: 变压器 - 317Front Insulator Cap: 前绝缘子帽 - 4386Tower (Lattice): 格构塔 - 1011Tower (Tucohy): Tucohy塔 - 483Transmission Line: 输电线路 - 517Damaged Line: 损坏的线路 - 247Bus: 总线 - 125Faulty Transformer: 故障变压器 - 6Spacer between Dampers: 阻尼器之间的间隔 - 51Broken Insulator: 破损的绝缘子 - 68Bent Insulator: 弯曲的绝缘子 - 20Object on TL: 输电线路上的物体 - 120Capacitor Bank: 电容器组 - 106Damaged Splice: 损坏的接头 - 83Image num: 图像数量 - 8932一、电力设施及缺陷检测数据集详情表数据集基础信息项目内容数据集名称电力设施及缺陷检测数据集图像总数8932 张标注格式YOLO、VOC 双格式类别总数19 类适用任务电力设备目标检测、缺陷故障识别类别标签及标注数量明细序号英文标签中文标签标注框数量0Insulator绝缘子34081Broken Insulator Cap破损的绝缘子帽20112Insulator Cap绝缘子帽227223Cable电缆246054Tower (Wooden)木塔8755Transformer变压器3176Front Insulator Cap前绝缘子帽43867Tower (Lattice)格构塔10118Tower (Tucohy)Tucohy塔4839Transmission Line输电线路51710Damaged Line损坏的线路24711Bus总线12512Faulty Transformer故障变压器613Spacer between Dampers阻尼器之间的间隔5114Broken Insulator破损的绝缘子6815Bent Insulator弯曲的绝缘子2016Object on TL输电线路上的物体12017Capacitor Bank电容器组10618Damaged Splice损坏的接头83类别名称列表YOLO 标签顺序names[Insulator,Broken Insulator Cap,Insulator Cap,Cable,Tower (Wooden),Transformer,Front Insulator Cap,Tower (Lattice),Tower (Tucohy),Transmission Line,Damaged Line,Bus,Faulty Transformer,Spacer between Dampers,Broken Insulator,Bent Insulator,Object on TL,Capacitor Bank,Damaged Splice]二、数据集应用场景无人机电力巡检架空线路、杆塔、绝缘子航拍图像智能检测自动识别破损、弯曲、线路损坏等缺陷替代人工高空巡检。变电站智能监控视频/图像实时检测变压器、电容器组、总线等设备状态预警故障设备。电力运维缺陷筛查批量归档图片自动化标注、缺陷分类统计提升运维检修效率。边缘端部署检测嵌入式设备、巡检终端本地运行模型现场快速判别电力设施故障。算法训练与科研电力视觉检测算法研发、模型对比、行业竞赛、教学实训数据集。三、YOLOv11 完整训练代码Python环境依赖# 安装依赖pipinstallultralytics opencv-python numpy pillow1. 数据集配置文件power.yaml放在数据集根目录下根据实际路径修改# 数据集路径path:./power_dataset# 数据集总根目录train:images/trainval:images/valtest:images/test# 类别数量与标签nc:19names:0:Insulator1:Broken Insulator Cap2:Insulator Cap3:Cable4:Tower (Wooden)5:Transformer6:Front Insulator Cap7:Tower (Lattice)8:Tower (Tucohy)9:Transmission Line10:Damaged Line11:Bus12:Faulty Transformer13:Spacer between Dampers14:Broken Insulator15:Bent Insulator16:Object on TL17:Capacitor Bank18:Damaged Splice2. 训练主代码train_yolov11.pyfromultralyticsimportYOLOdeftrain_power_detection():# 加载YOLOv11模型可选 n/s/m/l/x 不同大小模型modelYOLO(yolov11n.yaml)# 轻量化模型适合部署也可用 yolov11s.pt 预训练权重# 开始训练resultsmodel.train(datapower.yaml,# 数据集配置文件epochs100,# 训练轮数imgsz640,# 输入图像尺寸batch16,# 批次大小显存不足调小为8/4device0,# 使用GPU无GPU改为 devicecpuworkers4,# 数据加载线程patience15,# 早停轮数saveTrue,# 保存最优模型ampTrue,# 自动混合精度加速训练mosaic1.0,# 马赛克数据增强mixup0.0,copy_paste0.0,projectruns/train,# 训练结果保存目录namepower_defect_det,# 任务名称exist_okTrue)print(训练完成结果保存在 runs/train/power_defect_det 目录)if__name____main__:train_power_detection()3. 模型推理/测试代码predict.pyfromultralyticsimportYOLO# 加载训练好的权重modelYOLO(runs/train/power_defect_det/weights/best.pt)# 单张图片推理resultsmodel(test.jpg,saveTrue,conf0.25)# 文件夹批量推理# results model(./test_images/, saveTrue, conf0.25)# 视频推理# results model(test.mp4, saveTrue)print(推理完成结果已保存)四、补充说明数据分布说明电缆、绝缘子帽样本量极大故障变压器、弯曲绝缘子等缺陷样本极少训练时建议开启类别均衡、过采样或使用classify加权损失提升小类别精度。目录结构要求power_dataset/ ├── images/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels/ # YOLO标签txt文件 │ ├── train/ │ ├── val/ │ └── test/ └── power.yaml