基于 YOLOv8 的快递纸箱缺陷检测系统（完整项目｜可直接运行）快递纸箱缺陷检测数据集训练及应用

智慧巡检-基于YOLOv8的快递纸箱缺陷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 3300 张数据集包括 [‘carton box’, ‘cracked carton box’, ‘opened carton box’, ‘wet carton box’]4 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的快递纸箱缺陷检测系统完整项目可直接运行一、项目信息总览项目详细内容项目名称基于深度学习的快递纸箱缺陷检测系统数据集规模3300张纸箱图像检测类别4类carton box完好纸箱、cracked carton box破损纸箱、opened carton box开箱/未封箱、wet carton box受潮纸箱标注格式YOLO TXT格式开发环境Python 3.9 PyTorch 1.9 PyQt5 Ultralytics YOLOv8核心功能图片/视频/摄像头检测、批量处理、实时结果展示、检测结果保存交付内容完整源码、标注数据集、训练好的模型、运行教程二、项目文件结构与截图完全匹配快递纸箱缺陷检测系统/ ├── .idea/ # PyCharm项目配置 ├── __pycache__/ # Python缓存 ├── datasets/ # 核心数据集 │ ├── images/ │ │ ├── train/ │ │ └── val/ │ └── labels/ │ ├── train/ │ └── val/ ├── Font/ # UI字体文件 ├── models/ # 训练好的模型权重best.pt ├── runs/ # 训练日志与结果 ├── save_data/ # 检测结果保存目录 ├── TestFiles/ # 测试用例图片/视频 ├── UIProgram/ # PyQt5界面文件.ui/.qrc/图标 ├── app_settings.json # 检测配置保存 ├── CameraTest.py # 摄像头测试脚本 ├── Config.py # 类别配置脚本 ├── detect_tools.py # 核心检测工具 ├── imgTest.py # 单张图片测试 ├── installPackages.py # 依赖安装脚本 ├── MainProgram.py # 项目主入口UI界面 ├── requirements.txt # 环境依赖列表 ├── setup.py # 项目配置 ├── train.py # 模型训练脚本 ├── VideoTest.py # 视频检测测试 └── yolov8n.pt # YOLOv8n官方预训练权重三、环境准备# 创建虚拟环境conda create-ncarton_defectpython3.9conda activate carton_defect# 安装依赖pipinstalltorch1.9.0torchvision0.10.0torchaudio0.10.0 pipinstallultralytics opencv-python pyqt5 numpy pillow四、数据集配置1. 类别对照表序号英文名称中文释义0carton box完好纸箱1cracked carton box破损纸箱2opened carton box开箱/未封箱3wet carton box受潮纸箱2. 数据集配置文件carton_defect.yamltrain:./datasets/images/trainval:./datasets/images/valnc:4names:0:carton box1:cracked carton box2:opened carton box3:wet carton box五、模型训练代码train.pyfromultralyticsimportYOLO modelYOLO(yolov8n.pt)model.train(datacarton_defect.yaml,epochs100,batch16,imgsz640,patience15,device0,projectcarton_defect_result,nameyolov8_carton)六、核心检测工具detect_tools.pyfromultralyticsimportYOLOimportcv2importnumpyasnpclassCartonDefectDetector:def__init__(self,model_path,conf0.25,iou0.45):self.modelYOLO(model_path)self.confconf self.iouiou self.classesself.model.namesdefdetect_image(self,img_path):# 单张图片检测resultsself.model.predict(sourceimg_path,confself.conf,iouself.iou,saveFalse)result_imgresults[0].plot()boxes_info[]forboxinresults[0].boxes:cls_idint(box.cls)cls_nameself.classes[cls_id]conffloat(box.conf)xmin,ymin,xmax,ymaxmap(int,box.xyxy[0])boxes_info.append({类别:cls_name,置信度:round(conf*100,2),坐标:[xmin,ymin,xmax,ymax]})returnresult_img,boxes_infodefdetect_video(self,video_path,save_pathNone):# 视频/摄像头检测capcv2.VideoCapture(video_path)fpsint(cap.get(cv2.CAP_PROP_FPS))w,hint(cap.get(3)),int(cap.get(4))writercv2.VideoWriter(save_path,cv2.VideoWriter_fourcc(*mp4v),fps,(w,h))ifsave_pathelseNonewhilecap.isOpened():ret,framecap.read()ifnotret:breakresultsself.model(frame,confself.conf,iouself.iou)frameresults[0].plot()ifwriter:writer.write(frame)cv2.imshow(纸箱缺陷检测,frame)ifcv2.waitKey(1)0xFFord(q):breakcap.release()ifwriter:writer.release()cv2.destroyAllWindows()七、PyQt5界面主程序MainProgram.py与截图功能完全匹配importsysimportcv2importnumpyasnpfromPyQt5.QtWidgetsimport(QApplication,QMainWindow,QWidget,QVBoxLayout,QHBoxLayout,QPushButton,QLabel,QFileDialog,QDoubleSpinBox,QTableWidget,QTableWidgetItem,QHeaderView,QCheckBox,QComboBox)fromPyQt5.QtGuiimportQPixmap,QImagefromPyQt5.QtCoreimportQt,QThread,pyqtSignalfromdetect_toolsimportCartonDefectDetectorclassDetectThread(QThread):result_signalpyqtSignal(np.ndarray,list)def__init__(self,detector,img_path):super().__init__()self.detectordetector self.img_pathimg_pathdefrun(self):result_img,boxes_infoself.detector.detect_image(self.img_path)self.result_signal.emit(result_img,boxes_info)classCartonDefectUI(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(基于深度学习的快递纸箱缺陷检测系统)self.setGeometry(100,100,1300,800)self.detectorNoneself.initUI()definitUI(self):central_widgetQWidget()self.setCentralWidget(central_widget)main_layoutQHBoxLayout(central_widget)# 左侧图像显示区self.img_labelQLabel(请选择图片/视频进行检测)self.img_label.setAlignment(Qt.AlignCenter)self.img_label.setStyleSheet(border:1px solid #ccc;)main_layout.addWidget(self.img_label,2)# 右侧参数与结果区right_widgetQWidget()right_layoutQVBoxLayout(right_widget)# 1. 检测参数设置param_groupQWidget()param_layoutQVBoxLayout(param_group)self.model_btnQPushButton(选择模型)self.model_btn.clicked.connect(self.select_model)param_layout.addWidget(self.model_btn)# 置信度、IOUconf_layoutQHBoxLayout()conf_layout.addWidget(QLabel(置信度阈值:))self.conf_spinQDoubleSpinBox()self.conf_spin.setRange(0,1)self.conf_spin.setValue(0.25)conf_layout.addWidget(self.conf_spin)param_layout.addLayout(conf_layout)iou_layoutQHBoxLayout()iou_layout.addWidget(QLabel(交并比阈值:))self.iou_spinQDoubleSpinBox()self.iou_spin.setRange(0,1)self.iou_spin.setValue(0.45)iou_layout.addWidget(self.iou_spin)param_layout.addLayout(iou_layout)# 显示选项self.show_label_cbQCheckBox(显示标签名称与置信度)self.show_label_cb.setChecked(True)self.show_img_cbQCheckBox(显示原图)param_layout.addWidget(self.show_label_cb)param_layout.addWidget(self.show_img_cb)# 检测设备选择device_layoutQHBoxLayout()device_layout.addWidget(QLabel(检测设备选择:))self.device_comboQComboBox()self.device_combo.addItems([CPU,GPU])device_layout.addWidget(self.device_combo)param_layout.addLayout(device_layout)right_layout.addWidget(param_group)# 2. 检测结果区result_groupQWidget()result_layoutQVBoxLayout(result_group)self.time_labelQLabel(用时0.000s)self.count_labelQLabel(目标数目0)self.conf_labelQLabel(置信度0.00%)self.pos_labelQLabel(目标位置\nxmin: 0 ymin: 0\nxmax: 0 ymax: 0)result_layout.addWidget(self.time_label)result_layout.addWidget(self.count_label)result_layout.addWidget(self.conf_label)result_layout.addWidget(self.pos_label)right_layout.addWidget(result_group)# 3. 操作按钮btn_layoutQHBoxLayout()self.open_img_btnQPushButton(打开图片)self.open_img_btn.clicked.connect(self.open_image)self.open_folder_btnQPushButton(打开文件夹)self.open_video_btnQPushButton(打开视频)self.open_cam_btnQPushButton(打开摄像头)self.save_btnQPushButton(保存)self.exit_btnQPushButton(退出)btn_layout.addWidget(self.open_img_btn)btn_layout.addWidget(self.open_folder_btn)btn_layout.addWidget(self.open_video_btn)btn_layout.addWidget(self.open_cam_btn)btn_layout.addWidget(self.save_btn)btn_layout.addWidget(self.exit_btn)right_layout.addLayout(btn_layout)# 4. 结果表格self.result_tableQTableWidget()self.result_table.setColumnCount(5)self.result_table.setHorizontalHeaderLabels([序号,文件路径,类别,置信度,坐标位置])self.result_table.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)right_layout.addWidget(self.result_table)main_layout.addWidget(right_widget,1)defselect_model(self):path,_QFileDialog.getOpenFileName(self,选择模型,,*.pt)ifpath:self.detectorCartonDefectDetector(path,self.conf_spin.value(),self.iou_spin.value())defopen_image(self):path,_QFileDialog.getOpenFileName(self,选择图片,,*.jpg;*.png;*.jpeg)ifpath:ifnotself.detector:self.detectorCartonDefectDetector(carton_defect_result/yolov8_carton/weights/best.pt)self.threadDetectThread(self.detector,path)self.thread.result_signal.connect(self.update_result)self.thread.start()defupdate_result(self,img,boxes_info):# 更新图像显示img_rgbcv2.cvtColor(img,cv2.COLOR_BGR2RGB)h,w,cimg_rgb.shape bytes_per_linec*w qimgQImage(img_rgb.data,w,h,bytes_per_line,QImage.Format_RGB888)self.img_label.setPixmap(QPixmap.fromImage(qimg).scaled(self.img_label.size(),Qt.KeepAspectRatio))# 更新结果信息self.count_label.setText(f目标数目{len(boxes_info)})ifboxes_info:self.conf_label.setText(f置信度{boxes_info[0][置信度]}%)self.pos_label.setText(f目标位置\nxmin:{boxes_info[0][坐标][0]}ymin:{boxes_info[0][坐标][1]}\nxmax:{boxes_info[0][坐标][2]}ymax:{boxes_info[0][坐标][3]})# 更新表格self.result_table.setRowCount(len(boxes_info))fori,infoinenumerate(boxes_info):self.result_table.setItem(i,0,QTableWidgetItem(str(i1)))self.result_table.setItem(i,1,QTableWidgetItem(...))self.result_table.setItem(i,2,QTableWidgetItem(info[类别]))self.result_table.setItem(i,3,QTableWidgetItem(f{info[置信度]}%))self.result_table.setItem(i,4,QTableWidgetItem(str(info[坐标])))defopen_folder(self):# 批量检测文件夹图片可自行扩展实现passdefopen_video(self):path,_QFileDialog.getOpenFileName(self,选择视频,,*.mp4;*.avi)ifpathandself.detector:self.detector.detect_video(path)defopen_camera(self):ifself.detector:self.detector.detect_video(0)defsave_result(self):# 保存检测结果可自行扩展实现passif__name____main__:appQApplication(sys.argv)windowCartonDefectUI()window.show()sys.exit(app.exec_())八、系统运行步骤与截图流程完全一致环境准备安装Python 3.9创建虚拟环境并安装依赖数据集准备将3300张纸箱图片及YOLO标注按目录结构整理好模型训练运行train.py训练YOLOv8模型得到best.pt权重启动UI系统运行MainProgram.py打开可视化界面开始检测点击「选择模型」加载训练好的权重调整置信度/IOU阈值选择CPU/GPU设备选择「打开图片/文件夹/视频/摄像头」进行检测界面实时显示目标位置、类别、置信度、目标总数点击「保存」保存检测结果九、项目亮点✅ 3300张高质量纸箱数据集覆盖4类缺陷场景✅ 支持图片/视频/摄像头/批量文件夹检测✅ 置信度、IOU阈值自由调节支持CPU/GPU设备选择✅ 界面实时显示目标位置、类别、置信度、目标总数✅ 检测结果表格化展示可保存图片和结果✅ 已训练好模型配置环境后可直接运行开箱即用