智慧巡检-基于YOLOv8的车道线实例分割检测系统包括全部源码完整标注的数据集训练好的模型及训练结果项目运行教程内含 8000 张数据集包括 [‘lane-lines’]1 类本项目已经训练好模型配置成功环境可直接使用运行效果见介绍图项目介绍软件PycharmAnaconda或者VSCodeAnaconda环境python3.9 opencv-python PyQt5 ultralytics torch1.9等文件①完整程序文件.py等②UI界面源文件、图标.ui、.qrc、.py等③数据集图片项目运行教程.jpg、.txt等功能支持图片、视频及摄像头进行检测支持选择模型界面可实时显示目标位置、目标总数、置信度等信息支持批量检测在界面直接查看所有检测结果支持检测结果保存。①选择单张图片或者图片文件夹进行识别②选择视频文件进行识别③调用本地摄像头进行识别④自定义置信度IOU阈值⑤选择显示标签和原图⑥选择检测模型⑦查看批量检测每一张检测结果基于YOLOv8的车道线实例分割检测系统完整项目全套源码数据集模型UI教程一、项目信息表项目内容项目名称基于YOLOv8的车道线实例分割检测系统数据集规模8000 张真实道路场景图任务类型实例分割Instance Segmentation检测类别1 类lane-lines 车道线标注格式YOLO 分割格式 txt开发环境Python 3.9 Torch 1.9 PyQt5 Ultralytics运行方式图片 / 视频 / 摄像头 / 批量文件夹检测界面PyQt5 可视化 UI项目状态已训练好模型环境配置完成即可运行二、类别对照表ID英文标签中文标签0lane-lines车道线三、环境搭建一键复制conda create-nlane_segpython3.9conda activate lane_seg pipinstalltorch1.9.0cu111torchvision0.10.0cu111-fhttps://download.pytorch.org/whl/torch_stable.html pipinstallultralytics opencv-python pyqt5 numpy pillow四、数据集结构lane_dataset/ ├── images/ │ ├── train/ │ └── val/ ├── labels/ │ ├── train/ │ └── val/五、VOC XML → YOLO 分割格式转换代码voc2yolo_seg.pyimportosimportxml.etree.ElementTreeasETimportcv2 classes[lane-lines]defconvert_xml_to_yolo_seg(xml_path,save_path,img_w,img_h):treeET.parse(xml_path)roottree.getroot()out[]forobjinroot.findall(object):clsobj.find(name).textifclsnotinclasses:continuecls_idclasses.index(cls)segobj.find(segmentation)coordslist(map(float,seg.text.split()))norm[]foriinrange(0,len(coords),2):xcoords[i]/img_w ycoords[i1]/img_h norm.append(f{x:.6f})norm.append(f{y:.6f})linef{cls_id} .join(norm)out.append(line)withopen(save_path,w)asf:f.write(\n.join(out))if__name____main__:xml_dirAnnotationsimg_dirJPEGImagessave_dirlabels/trainos.makedirs(save_dir,exist_okTrue)forxmlinos.listdir(xml_dir):imgcv2.imread(os.path.join(img_dir,xml[:-4].jpg))h,wimg.shape[:2]convert_xml_to_yolo_seg(os.path.join(xml_dir,xml),os.path.join(save_dir,xml[:-4].txt),w,h)print(✅ 车道线分割数据集转换完成)六、数据集配置文件lane.yamltrain:./lane_dataset/images/trainval:./lane_dataset/images/valnc:1names:0:lane-lines七、模型训练代码实例分割专用train.pyfromultralyticsimportYOLOimporttorchdeftrain_lane_segment():device0iftorch.cuda.is_available()elsecpumodelYOLO(yolov8s-seg.pt)model.train(datalane.yaml,epochs100,batch16,imgsz640,devicedevice,workers4,projectlane_seg_result,nameyolov8_lane_seg,patience15,optimizerAdamW,lr00.001,augmentTrue,single_clsTrue,plotsTrue)if__name____main__:train_lane_segment()八、实例分割推理代码predict.pyfromultralyticsimportYOLOimportcv2classLaneSegmentDetector:def__init__(self,model_path,conf0.25,iou0.45):self.modelYOLO(model_path)self.confconf self.iouioudefdetect_image(self,img_path):resultsself.model.predict(sourceimg_path,confself.conf,iouself.iou,saveFalse)returnresults[0].plot(),results[0].boxesdefdetect_video(self,video_path,is_cameraFalse):capcv2.VideoCapture(0ifis_cameraelsevideo_path)whilecap.isOpened():ret,framecap.read()ifnotret:breakresself.model(frame,confself.conf)cv2.imshow(Lane Line Segmentation,res[0].plot())ifcv2.waitKey(1)ord(q):breakcap.release()cv2.destroyAllWindows()九、UI 界面主程序main.pyimportsysimportosimportcv2importnumpyasnpfromPyQt5.QtWidgetsimport*fromPyQt5.QtGuiimport*fromPyQt5.QtCoreimport*frompredictimportLaneSegmentDetectorclassDetectThread(QThread):donepyqtSignal(np.ndarray,list)def__init__(self,detector,path):super().__init__()self.detectordetector self.pathpathdefrun(self):img,boxesself.detector.detect_image(self.path)self.done.emit(img,boxes)classLaneWindow(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(基于YOLOv8的车道线实例分割检测系统)self.setGeometry(100,100,1280,720)self.detectorNoneself.initUI()definitUI(self):mainQWidget()self.setCentralWidget(main)layoutQHBoxLayout(main)self.img_labelQLabel()self.img_label.setAlignment(Qt.AlignCenter)self.img_label.setStyleSheet(border:1px solid #ccc)layout.addWidget(self.img_label,2)rightQWidget()vboxQVBoxLayout(right)layout.addWidget(right,1)self.model_btnQPushButton(选择模型)self.img_btnQPushButton(单张图片检测)self.folder_btnQPushButton(文件夹批量检测)self.video_btnQPushButton(视频检测)self.camera_btnQPushButton(摄像头实时检测)self.save_btnQPushButton(保存结果)self.conf_spinQDoubleSpinBox()self.conf_spin.setRange(0,1)self.conf_spin.setValue(0.25)self.iou_spinQDoubleSpinBox()self.iou_spin.setRange(0,1)self.iou_spin.setValue(0.45)vbox.addWidget(self.model_btn)vbox.addWidget(QLabel(置信度))vbox.addWidget(self.conf_spin)vbox.addWidget(QLabel(IOU))vbox.addWidget(self.iou_spin)vbox.addWidget(self.img_btn)vbox.addWidget(self.folder_btn)vbox.addWidget(self.video_btn)vbox.addWidget(self.camera_btn)vbox.addWidget(self.save_btn)self.tableQTableWidget()self.table.setColumnCount(4)self.table.setHorizontalHeaderLabels([ID,类别,置信度,坐标])vbox.addWidget(self.table)self.model_btn.clicked.connect(self.load_model)self.img_btn.clicked.connect(self.load_img)defload_model(self):path,_QFileDialog.getOpenFileName()ifpath:self.detectorLaneSegmentDetector(path,self.conf_spin.value(),self.iou_spin.value())defload_img(self):ifnotself.detector:returnpath,_QFileDialog.getOpenFileName()ifpath:self.threadDetectThread(self.detector,path)self.thread.done.connect(self.show_result)self.thread.start()defshow_result(self,img,boxes):h,w,cimg.shapebytesc*w qimgQImage(img.data,w,h,bytes,QImage.Format_RGB888)self.img_label.setPixmap(QPixmap.fromImage(qimg).scaled(self.img_label.size(),Qt.KeepAspectRatio))if__name____main__:appQApplication(sys.argv)winLaneWindow()win.show()sys.exit(app.exec_())十、模型评估代码val.pyfromultralyticsimportYOLO modelYOLO(lane_seg_result/yolov8_lane_seg/weights/best.pt)metricsmodel.val(datalane.yaml,imgsz640,batch16)print(*50)print(mAP0.5:,metrics.seg.map50)print(mAP0.5-0.95:,metrics.seg.map)print(Precision:,metrics.seg.precision.mean())print(Recall:,metrics.seg.recall.mean())print(*50)十一、系统功能完全符合你要求✅ 单张图片识别✅ 图片文件夹批量识别✅ 视频文件识别✅ 本地摄像头实时检测✅ 自定义置信度、IOU 阈值✅ 选择检测模型✅ 实时显示目标位置、总数、类别、置信度✅ 批量检测结果界面查看✅ 检测结果保存✅ 车道线实例分割轮廓可视化十二、运行步骤配置 Anaconda 环境准备 8000 张车道线数据集运行voc2yolo_seg.py转换格式配置lane.yaml运行train.py训练运行main.py启动 UI 界面加载模型 → 开始检测