知乎煤矿矿井监控数据集YOLOv11训练完整代码 YOLOv11训练完整代码 对煤矿工人、压缩氧气自救装置、采矿头盔、钻杆、钻机、矿工与钻杆交互

c对煤矿工人、压缩氧气自救装置、采矿头盔、钻杆、钻机、矿工与钻杆交互情况进行标注累计超过10万帧视频图像进行标注VOC和coco两种标注格式数据集共34GB数据量数据1煤矿工业场景煤矿工人检测数据集标注类别coal_miner训练集12461张图像验证集3145张图像5GB数据2煤矿工业场景钻杆检测数据集标注类别drill_pipe训练集21488张图像验证集5390张图像9GB数据3煤矿工业场景钻机检测数据集标注类别drill_rig训练集12224张图像验证集3087张图像5GB数据4煤矿工业场景煤矿工人与钻杆交互检测数据集标注类别操作钻杆拆卸钻杆训练集12461张图像验证集3145张图像5GB数据5煤矿工业场景采矿头盔检测数据集标注类别mining_helmet训练集10719张图像验证集2713张图像4.5GB数据6煤矿工业场景压缩氧气自救装置检测数据集标注类别oxygen_self_rescue训练集15809张图像验证集4000张图像6.5GB一、煤矿工业场景全面安全监控数据集总览表总数据量34GB10万帧图像标注格式VOC‑xml、COCO‑json适用模型YOLOv11 / YOLOv8 / RT‑DETR等目标检测网络子数据集编号数据集名称标注类别标签训练集图片验证集图片磁盘大小核心应用场景1煤矿工人检测数据集coal_miner煤矿工人1246131455GB井下人员越界检测、人员在岗监测、人员闯入危险区域告警、井下人员计数、人员轨迹追踪、人员安全区域管控2钻杆检测数据集drill_pipe钻杆2148853909GB钻杆物料盘点、钻杆遗留隐患检测、钻杆掉落识别、物料库存监控、工作面设备物料安全巡检3钻机检测数据集drill_rig钻机1222430875GB钻机运行状态监测、钻机违规启停识别、钻机区域入侵预警、大型采掘设备安防监控、设备故障可视化巡检4矿工‑钻杆交互检测数据集operate‑drill操作钻杆、disassemble‑drill拆卸钻杆1246131455GB工人操作合规性检测、高危作业行为识别、违章作业监控、人机协同作业安全预警、作业流程智能监管5采矿头盔检测数据集mining_helmet采矿头盔1071927134.5GB矿工安全帽佩戴识别、未戴头盔违规告警、井下人员身份辅助识别、安全防护装备智能检查6压缩氧气自救装置检测数据集oxygen_self_rescue压缩氧气自救装置1580940006.5GB自救装备佩戴检查、应急物资缺失告警、逃生装备携带合规检测、灾害应急物资智能巡检汇总统计图像总数 124613145 214885390 122243087 124613145 107192713 158094000 103642 张图像超10万帧全部子数据集均无独立测试集训练评估可从训练集再划分10%作为测试子集二、合并后6合1多任务煤矿安全数据集 yaml配置文件mine_safety.yamlpath:./datasets/mine_safetytrain:images/trainval:images/val# 可自行划分测试集test:images/testnames:0:coal_miner1:drill_pipe2:drill_rig3:operate‑drill4:disassemble‑drill5:mining_helmet6:oxygen_self_rescue三、YOLOv11训练代码train_mine.pyfromultralyticsimportYOLO#加载轻量化模型煤矿监控视频场景推荐yolo11s平衡精度速度modelYOLO(yolo11s.pt)resultsmodel.train(datamine_safety.yaml,epochs120,imgsz640,batch12,device0,workers4,patience20,mosaic1.0,mixup0.15,copy_paste0.15,hsv_h0.015,hsv_s0.7,hsv_v0.4,projectruns/train,namecoal_mine_safety_detect)四、测试评估脚本test_mine.pyfromultralyticsimportYOLO modelYOLO(./runs/train/coal_mine_safety_detect/weights/best.pt)metricsmodel.val(splittest)print(fmAP0.5{metrics.box.map50:.3f})print(fmAP0.5‑0.95{metrics.box.map:.3f})五、推理预测脚本predict_mine.pyfromultralyticsimportYOLO modelYOLO(./runs/train/coal_mine_safety_detect/weights/best.pt)#图片检测resmodel.predict(sourcemine_test.jpg,saveTrue,conf0.4)#井下监控视频流检测#res model.predict(sourcemine_video.mp4,saveTrue,conf0.4)#实时摄像头检测#res model.predict(source0,saveTrue,conf0.4)六、PyQt5井下安全监控可视化简易界面代码importsysimportcv2fromPyQt5.QtWidgetsimport(QApplication,QMainWindow,QPushButton,QLabel,QFileDialog,QTextEdit)fromPyQt5.QtGuiimportQImage,QPixmapfromPyQt5.QtCoreimportQt,QThread,pyqtSignalfromultralyticsimportYOLO modelYOLO(./runs/train/coal_mine_safety_detect/weights/best.pt)classMineDetThread(QThread):send_framepyqtSignal(object)send_resultpyqtSignal(list)def__init__(self,source):super().__init__()self.sourcesource self.flagTruedefrun(self):capcv2.VideoCapture(self.source)whileself.flag:ret,framecap.read()ifnotret:breakresultmodel(frame,conf0.4)boxesresult[0].boxes info[]forboxinboxes:x1,y1,x2,y2map(int,box.xyxy[0])conffloat(box.conf[0])clsint(box.cls[0])info.append([x1,y1,x2,y2,conf,cls])cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)cv2.putText(frame,f{model.names[cls]}{conf:.2f},(x1,y1-8),cv2.FONT_HERSHEY_SIMPLEX,0.45,(0,0,255),1)self.send_frame.emit(frame)self.send_result.emit(info)cap.release()classMineMonitorWindow(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(煤矿井下安全智能监控系统)self.resize(1250,820)self.init_ui()self.det_threadNonedefinit_ui(self):self.show_labelQLabel(self)self.show_label.setGeometry(20,60,780,650)self.show_label.setStyleSheet(border:1px solid #777;)self.btn_imgQPushButton(图片检测,self)self.btn_img.setGeometry(840,60,200,40)self.btn_img.clicked.connect(self.detect_image)self.btn_videoQPushButton(监控视频检测,self)self.btn_video.setGeometry(840,120,200,40)self.btn_video.clicked.connect(self.detect_video)self.btn_camQPushButton(井下摄像头实时检测,self)self.btn_cam.setGeometry(840,180,200,40)self.btn_cam.clicked.connect(self.detect_cam)self.result_textQTextEdit(self)self.result_text.setGeometry(840,250,340,450)self.result_text.setPlaceholderText(井下目标检测结果类别、置信度、坐标信息)defrender_frame(self,frame):rgbcv2.cvtColor(frame,cv2.COLOR_BGR2RGB)h,w,chrgb.shape qimgQImage(rgb.data,w,h,ch*w,QImage.Format_RGB888)self.show_label.setPixmap(QPixmap.fromImage(qimg).scaled(self.show_label.size(),Qt.KeepAspectRatio))defprint_result(self,det_info):self.result_text.clear()totallen(det_info)self.result_text.append(f识别目标总数{total}\n)foridx,iteminenumerate(det_info):x1,y1,x2,y2,conf,clsitem namemodel.names[cls]self.result_text.append(f[{idx1}]目标:{name},置信度:{conf:.2f}\n坐标:xmin{x1},ymin{y1},xmax{x2},ymax{y2}\n)defdetect_image(self):path,_QFileDialog.getOpenFileName(self,打开井下图像,,Images(*.jpg *.png))ifnotpath:returnframecv2.imread(path)resmodel(frame,conf0.4)box_listres[0].boxes info[]forboxinbox_list:x1,y1,x2,y2map(int,box.xyxy[0])conffloat(box.conf[0])clsint(box.cls[0])info.append([x1,y1,x2,y2,conf,cls])cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)cv2.putText(frame,f{model.names[cls]}{conf:.2f},(x1,y1-8),cv2.FONT_HERSHEY_SIMPLEX,0.45,(0,0,255),1)self.render_frame(frame)self.print_result(info)defdetect_video(self):path,_QFileDialog.getOpenFileName(self,打开监控视频,,Video(*.mp4 *.avi))ifnotpath:returnself.det_threadMineDetThread(path)self.det_thread.send_frame.connect(self.render_frame)self.det_thread.send_result.connect(self.print_result)self.det_thread.start()defdetect_cam(self):self.det_threadMineDetThread(0)self.det_thread.send_frame.connect(self.render_frame)self.det_thread.send_result.connect(self.print_result)self.det_thread.start()if__name____main__:appQApplication(sys.argv)winMineMonitorWindow()win.show()sys.exit(app.exec_())