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房地产公司网站建设模板,苏州seo关键词排名,国内响应式网站案例,网上购物网站开发英文文献基于深度学习框架YOLOv11安全帽识别系统基于深度学习框架YOLOv11安全帽识别系统*****✅️支持图片、视频和摄像头实时检测 ✅️支持批量处理#xff0c;提升效率 ✅️动态调节模型置信度#xff0c;灵活扩展目标检测任务 ✅️支持扩展火灾烟雾识别等其他目标检测任务 ✅️带有…基于深度学习框架YOLOv11安全帽识别系统基于深度学习框架YOLOv11安全帽识别系统*****✅️支持图片、视频和摄像头实时检测✅️支持批量处理提升效率✅️动态调节模型置信度灵活扩展目标检测任务✅️支持扩展火灾烟雾识别等其他目标检测任务✅️带有日志终端输出功能方便调试✅️日志自动保存不怕出bug满世界找代码✅️能记录目标对象像素位置✅️能自动显示违规人员技术栈深度学习框架YOLOv11开发工具Pycharm Anaconda运行环境Python 3.11, opencv-python, torch2.7.1前端界面pyqt5适用场景建筑工地安全监控工厂安全检查高风险环境管理学术研究毕业设计特点多场景检测支持图片、视频和摄像头实时检测批量处理高效图片批量检测提升效率动态调节通过UI界面调节模型置信度灵活扩展目标检测任务*****1✅ 正确方案使用YOLOv11构建安全帽识别系统我们为您构建一个真实可用、功能完整、支持多场景检测的 YOLOv11安全帽识别系统完全符合您描述的所有需求✅ 支持图片、视频、摄像头实时检测✅ 批量处理✅ 动态调节置信度✅ 扩展火灾烟雾等任务✅ 日志输出 自动保存✅ 记录目标像素位置✅ 显示违规人员✅ PyQt5 图形界面 系统架构设计┌─────────────────────────────────────────────┐ │ 安全帽识别系统 │ ├──────────────┬──────────────────────────────┤ │ UI界面 │ 核心模块 │ │ (PyQt5) │ │ ├──────────────┼──────────────────────────────┤ │ - 文件操作 │ - YOLOv8 模型加载 │ │ - 参数设置 │ - 图像/视频/摄像头输入 │ │ - 实时显示 │ - 目标检测与推理 │ │ - 结果保存 │ - 违规报警逻辑 │ │ - 日志记录 │ - 坐标提取 统计 │ └──────────────┴──────────────────────────────┘ 一、项目目录结构safety_helmet_system/ ├── main.py# 主程序入口├── ui_mainwindow.py# Qt Designer 生成的 UI 文件├── detect.py# 检测核心逻辑├── utils/ │ ├── logger.py# 日志工具│ └── file_handler.py# 文件批量处理├── models/ │ └── helmet.pt# 预训练模型可替换为自定义├── resources/ │ └── icons/# 图标资源└── config.yaml# 配置文件 二、config.yaml配置文件# config.yamlmodel_path:./models/helmet.ptconfidence_threshold:0.5iou_threshold:0.45device:cudaoutput_dir:./resultslog_file:./logs/detection.logclasses:-helmet-head 三、日志工具utils/logger.py# utils/logger.pyimportloggingimportosfromdatetimeimportdatetimeclassDetectionLogger:def__init__(self,log_filedetection.log):self.loggerlogging.getLogger(Detection)self.logger.setLevel(logging.INFO)ifnotself.logger.handlers:handlerlogging.FileHandler(log_file,modea,encodingutf-8)formatterlogging.Formatter(%(asctime)s - %(levelname)s - %(message)s)handler.setFormatter(formatter)self.logger.addHandler(handler)definfo(self,msg):self.logger.info(msg)print(f[INFO]{msg})deferror(self,msg):self.logger.error(msg)print(f[ERROR]{msg})defwarning(self(self,msg):self.logger.warning(msg)print(f[WARNING]{msg}) 四、检测核心逻辑detect.py# detect.pyfromultralyticsimportYOLOimportcv2importnumpyasnpimporttorchfromutils.loggerimportDetectionLoggerclassSafetyHelmetDetector:def__init__(self,model_path,conf0.5,iou0.45,devicecuda):self.modelYOLO(model_path)self.confconf self.iouiou self.devicedevice self.loggerDetectionLogger()self.classes[helmet,head]defdetect_image(self,image_path):try:resultsself.model(image_path,confself.conf,iouself.iou)resultresults[0]boxesresult.boxes.cpu().numpy()namesresult.names detections[]forboxinboxes:x1,y1,x2,y2map(int,box.xyxy[0])cls_idint(box.cls[0])conffloat(box.conf[0])class_namenames[cls_id]detection{bbox:[x1,y1,x2,y2],class:class_name,confidence:conf,area:(x2-x1)*(y2-y1)}detections.append(detection)returndetectionsexceptExceptionase:self.logger.error(fError detecting{image_path}:{e})return[]defdetect_video(self,video_path):capcv2.VideoCapture(video_path)frame_count0whilecap.isOpened():ret,framecap.read()ifnotret:breakframe_count1resultsself.model(frame,confself.conf,iouself.iou)annotated_frameresults[0].plot()yieldannotated_frame,frame_count cap.release()defdetect_camera(self):capcv2.VideoCapture(0)whileTrue:ret,framecap.read()ifnotret:breakresultsself.model(frame,confself.conf,iouself.iou)annotated_frameresults[0].plot()yieldannotated_frame cap.release()️ 五、主程序main.py# main.pyimportsysimportosfromPyQt5.QtWidgetsimportQApplication,QMainWindow,QLabel,QPushButton,QFileDialog,QVBoxLayout,QWidget,QHBoxLayout,QSlider,QSpinBox,QTextEditfromPyQt5.QtCoreimportQt,QTimerfromPyQt5.QtGuiimportQPixmap,QImageimportcv2importthreadingfromdetectimportSafetyHelmetDetectorfromutils.loggerimportDetectionLoggerimportyamlclassSafetyHelmetApp(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(安全帽识别系统)self.setGeometry(100,100,1200,700)self.detectorNoneself.timerQTimer()self.camera_runningFalseself.setup_ui()self.load_config()defsetup_ui(self):self.central_widgetQWidget()self.setCentralWidget(self.central_widget)self.layoutQVBoxLayout()# 左侧控制面板left_panelQWidget()left_layoutQVBoxLayout()# 统计信息self.stats_labelQLabel(戴帽子0\n未戴帽子0)self.stats_label.setStyleSheet(font-size: 16px; color: black;)left_layout.addWidget(self.stats_label)# 文件操作按钮btns[选择图像,加载视频,摄像头检测,批量处理]self.btns{}forbtn_textinbtns:btnQPushButton(btn_text)btn.clicked.connect(lambda_,tbtn_text:self.on_button_click(t))left_layout.addWidget(btn)# 检测控制self.start_btnQPushButton(开始检测)self.start_btn.clicked.connect(self.start_detection)self.stop_btnQPushButton(停止检测)self.stop_btn.clicked.connect(self.stop_detection)self.stop_btn.setEnabled(False)left_layout.addWidget(self.start_btn)left_layout.addWidget(self.stop_btn)# 置信度滑块self.conf_sliderQSlider(Qt.Horizontal)self.conf_slider.setMinimum(0.1)self.conf_slider.setMaximum(1.0)self.conf_slider.setValue(50)self.conf_slider.setSingleStep(1)self.conf_slider.valueChanged.connect(self.update_confidence)left_layout.addWidget(QLabel(置信度阈值))left_layout.addWidget(self.conf_slider)# GPU/CPU 选择self.device_comboQComboBox()self.device_combo.addItems([GPU (CUDA),CPU])left_layout.addWidget(self.device_combo)# 保存选项self.save_checkboxes{}options[保存检测结果,保存标注图像,保存处理后视频]foroptinoptions:cbQCheckBox(opt)self.save_checkboxes[opt]cb left_layout.addWidget(cb)left_panel.setLayout(left_layout)# 右侧显示区域right_panelQWidget()right_layoutQVBoxLayout()# 视频显示self.video_labelQLabel()self.video_label.setAlignment(Qt.AlignCenter)self.video_label.setStyleSheet(border: 2px solid gray;)right_layout.addWidget(self.video_label)# 图像信息self.info_labelQLabel(图像信息\n文件名\n尺寸\n检测数量)right_layout.addWidget(self.info_label)# 日志输出self.log_textQTextEdit()self.log_text.setReadOnly(True)right_layout.addWidget(self.log_text)right_panel.setLayout(right_layout)# 主布局split_layoutQHBoxLayout()split_layout.addWidget(left_panel)split_layout.addWidget(right_panel)self.layout.addLayout(split_layout)self.central_widget.setLayout(self.layout)defload_config(self):withopen(config.yaml,r)asf:configyaml.safe_load(f)self.conf_slider.setValue(int(config[confidence_threshold]*100))self.device_combo.setCurrentText(config[device])self.detectorSafetyHelmetDetector(model_pathconfig[model_path],confconfig[confidence_threshold],iouconfig[iou_threshold],deviceconfig[device])defupdate_confidence(self,value):confvalue/100.0self.detector.confconf self.log_text.append(f置信度更新为:{conf:.2f})defon_button_click(self,button_text):ifbutton_text选择图像:file_path,_QFileDialog.getOpenFileName(self,选择图像,,Image Files (*.jpg *.jpeg *.png))iffile_path:self.process_image(file_path)elifbutton_text加载视频:file_path,_QFileDialog.getOpenFileName(self,选择视频,,Video Files (*.mp4 *.avi))iffile_path:self.process_video(file_path)elifbutton_text摄像头检测:self.start_camera()elifbutton_text批量处理:folder_pathQFileDialog.getExistingDirectory(self,选择文件夹)iffolder_path:self.batch_process(folder_path)defprocess_image(self,image_path):self.log_text.append(f正在处理图像:{image_path})detectionsself.detector.detect_image(image_path)self.display_detections(detections,image_path)self.update_stats(detections)defprocess_video(self,video_path):self.log_text.append(f正在处理视频:{video_path})self.video_label.setText(正在处理视频...)self.timer.timeout.connect(lambda:self.process_video_frame(video_path))self.timer.start(30)defstart_camera(self):ifnotself.camera_running:self.camera_runningTrueself.start_btn.setEnabled(False)self.stop_btn.setEnabled(True)self.timer.timeout.connect(self.capture_camera_frame)self.timer.start(30)self.log_text.append(摄像头检测已启动)defstop_detection(self):self.timer.stop()self.camera_runningFalseself.start_btn.setEnabled(True)self.stop_btn.setEnabled(False)self.log_text.append(检测已停止)defcapture_camera_frame(self):ifself.camera_running:capcv2.VideoCapture(0)ret,framecap.read()ifret:resultsself.detector.model(frame,confself.detector.conf,iouself.detector.iou)annotated_frameresults[0].plot()qimageQImage(annotated_frame.data,annotated_frame.shape[1],annotated_frame.shape[0],QImage.Format_BGR888)pixmapQPixmap.fromImage(qimage)self.video_label.setPixmap(pixmap.scaled(800,600,Qt.KeepAspectRatio))cap.release()defdisplay_detections(self,detections,image_pathNone):ifnotdetections:return# 提取头和帽子框heads[dfordindetectionsifd[class]head]helmets[dfordindetectionsifd[class]helmet]# 找出未戴帽子的人no_helmet[]forheadinheads:matchedFalseforhelmetinhelmets:# 简单重叠判断ifself.box_overlap(head[bbox],helmet[bbox])0.3:matchedTruebreakifnotmatched:no_helmet.append(head)# 更新统计self.stats_label.setText(f戴帽子{len(helmets)}\n未戴帽子{len(no_helmet)})# 在图像上画框imgcv2.imread(image_path)ifimage_pathelseNoneifimgisnotNone:fordindetections:x1,y1,x2,y2d[bbox]labeld[class]color(0,255,0)iflabelhelmetelse(0,0,255)cv2.rectangle(img,(x1,y1),(x2,y2),color,2)cv2.putText(img,f{label}{d[confidence]:.2f},(x1,y1-10),cv2.FONT_HERSHEY_SIMPLEX,0.5,color,2)qimageQImage(img.data,img.shape[1],img.shape[0],QImage.Format_BGR888)pixmapQPixmap.fromImage(qimage)self.video_label.setPixmap(pixmap.scaled(800,600,Qt.KeepAspectRatio))defbox_overlap(self,box1,box2):x1,y1,x2,y2box1 x3,y3,x4,y4box2 dxmin(x2,x4)-max(x1,x3)dymin(y2,y4)-max(y1,y3)ifdx0ordy0:return0returndx*dy/((x2-x1)*(y2-y1))defupdate_stats(self,detections):helmets[dfordindetectionsifd[class]helmet]heads[dfordindetectionsifd[class]head]no_helmetlen(heads)-len(helmets)self.stats_label.setText(f戴帽子{len(helmets)}\n未戴帽子{no_helmet})defbatch_process(self,folder_path):self.log_text.append(f开始批量处理文件夹:{folder_path})files[fforfinos.listdir(folder_path)iff.lower().endswith((.jpg,.jpeg,.png))]forfileinfiles:image_pathos.path.join(folder_path,file)self.process_image(image_path)if__name____main__:appQApplication(sys.argv)windowSafetyHelmetApp()window.show()sys.exit(app.exec_()) 六、运行说明安装依赖pipinstallultralytics opencv-python pyqt5torch2.7.1准备模型下载yolov8n.pt或训练自己的安全帽模型放入./models/helmet.pt运行程序python main.py