基于 YOLOv11 集装箱箱号与 ISO 代码区域检测系统集装箱箱号和ISO代码区域检测数据集2376张提供yolovoccoco三种标注方式图像尺寸:1599*1200类别数量:2类训练集图像数量:2208; 验证集图像数量:130 测试集图像数量:38类别名称: 每一类图像数 每一类标注数container_number-集装箱箱号2325,2603iso_code-集装箱ISO代码2195,2238image num: 2376模型代码采用 YOLOv11n 网络训练训练轮次80 个 epoch提供全部训练 测试源代码训练精度 mAP 效果如图所示PyQt5 界面功能界面使用 PyQt5 开发提供全部源码.ui、.qrc、.py 及图标文件支持图片检测、视频检测、摄像头实时检测界面实时显示目标位置、目标总数、置信度等信息支持检测结果保存导出操作简单直观无需命令行4、运行环境Python3.8、opencv-python、PyQt5、torch支持 Windows、Linux 系统基于YOLOv11 集装箱箱号与ISO代码区域检测系统1、数据集配置文件container.yamlpath:./container_datasettrain:images/trainval:images/valtest:images/testnames:0:container_number1:iso_code2、训练脚本train_container.pyfromultralyticsimportYOLOif__name____main__:modelYOLO(yolo11n.pt)resultmodel.train(datacontainer.yaml,epochs80,imgsz640,batch8,device0,workers2,projectcontainer_result,nameyolo11_container,ampTrue,mosaic0.8,conf0.25)3、图片推理脚本predict_container.pyfromultralyticsimportYOLOimportcv2 modelYOLO(./container_result/yolo11_container/weights/best.pt)defcontainer_detect(img_path,save_namecontainer_out.jpg):resmodel(img_path)[0]draw_imgres.plot()cv2.imwrite(save_name,draw_img)returndraw_img,res.boxesif__name____main__:container_detect(test_container.jpg)4、PyQt5可视化界面container_gui.pyimportsysimportcv2fromPyQt5.QtWidgetsimport(QApplication,QMainWindow,QPushButton,QLabel,QFileDialog,QTableWidget,QTableWidgetItem,QComboBox,QGroupBox,QVBoxLayout,QHBoxLayout,QWidget)fromPyQt5.QtGuiimportQImage,QPixmapfromPyQt5.QtCoreimportQtfromultralyticsimportYOLOclassContainerDetectUI(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(基于YOLOv11的集装箱箱号和iso代码区域检测系统)self.resize(1350,950)self.modelYOLO(./container_result/yolo11_container/weights/best.pt)self.init_ui()definit_ui(self):main_widgetQWidget()self.setCentralWidget(main_widget)main_layoutQHBoxLayout(main_widget)left_layoutQVBoxLayout()self.img_labelQLabel(图像显示区域)self.img_label.setFixedSize(820,640)left_layout.addWidget(self.img_label)self.tableQTableWidget()self.table.setColumnCount(5)self.table.setHorizontalHeaderLabels([序号,类别,置信度,坐标,文件路径])left_layout.addWidget(self.table)right_layoutQVBoxLayout()group_fileQGroupBox(文件导入)self.btn_imgQPushButton(选择图片)self.btn_img.clicked.connect(self.load_img)self.btn_videoQPushButton(选择视频)self.btn_camQPushButton(开启摄像头)v1QVBoxLayout()v1.addWidget(self.btn_img)v1.addWidget(self.btn_video)v1.addWidget(self.btn_cam)group_file.setLayout(v1)group_infoQGroupBox(检测结果)self.label_timeQLabel(用时0 s | 目标数目0)self.combo_clsQComboBox()self.combo_cls.addItems([全部,container_number,iso_code])self.label_posQLabel(xmin:0 ymin:0 xmax:0 ymax:0)self.label_confQLabel(置信度0%)self.btn_saveQPushButton(保存结果)v2QVBoxLayout()v2.addWidget(self.label_time)v2.addWidget(self.combo_cls)v2.addWidget(self.label_pos)v2.addWidget(self.label_conf)v2.addWidget(self.btn_save)group_info.setLayout(v2)right_layout.addWidget(group_file)right_layout.addWidget(group_info)main_layout.addLayout(left_layout)main_layout.addLayout(right_layout)defload_img(self):fpath,_QFileDialog.getOpenFileName(self,打开图片,.,图片(*.jpg *.png *.jpeg))ifnotfpath:returnpredmodel(fpath,conf0.25)[0]draw_imgpred.plot()self.show_img(draw_img,self.img_label)self.table.setRowCount(0)foridx,boxinenumerate(pred.boxes):cls_namepred.names[int(box.cls)]conffloat(box.conf)xyxybox.xyxy[0].cpu().numpy()self.table.insertRow(idx)self.table.setItem(idx,0,QTableWidgetItem(str(idx1)))self.table.setItem(idx,1,QTableWidgetItem(cls_name))self.table.setItem(idx,2,QTableWidgetItem(f{conf:.2f}))self.table.setItem(idx,3,QTableWidgetItem(f{xyxy.round(0)}))self.table.setItem(idx,4,QTableWidgetItem(fpath))self.label_time.setText(f用时{pred.speed[inference]/1000:.3f}s | 目标数目{len(pred.boxes)})defshow_img(self,cv_img,label):rgb_imgcv2.cvtColor(cv_img,cv2.COLOR_BGR2RGB)h,w,crgb_img.shape qimgQImage(rgb_img.data,w,h,w*c,QImage.Format_RGB888)label.setPixmap(QPixmap.fromImage(qimg).scaled(label.size(),Qt.KeepAspectRatio))if__name____main__:appQApplication(sys.argv)winContainerDetectUI()win.show()sys.exit(app.exec_())环境安装pipinstallultralytics torch opencv-python pyqt5关键词标签集装箱箱号检测ISO代码识别YOLOv11港口集装箱监控PyQt5可视化集装箱编号定位应用场景港口、堆场闸口集装箱箱号自动定位识别箱号与ISO代码区域白天/夜间多光照下集装箱图像检测闸口摄像头实时视频流检测集装箱图像离线批量分析输出坐标、类别、置信度