智慧航拍-无人机影像松材线虫病目标检测数据集共采集100余幅大尺寸航空影像50004000左右并提供voc和yolo两种标注在此基础上切分为640640小图共4000余幅图提供VOC和yolo标注,3GB数据量pyqt5界面和基于yolo11n的训练模型代码一、数据集信息结构化表格松材线虫病航拍检测数据集详情表项目参数详情数据集名称无人机航拍松材线虫病树目标检测数据集原始影像数量100 张大尺寸航空航拍图原始分辨率5000 × 4000 像素超高分辨率林区航拍切片后图像总数4000 张切片规格640 × 640 像素适配YOLO模型输入尺寸总数据体积约3GB标注格式YOLO txt格式 VOC XML 双格式兼容检测目标类别unhealthy染病枯死松树单类别病害检测模型基准YOLOv11-n轻量化模型精度指标mAP0.5 0.918P-R曲线性能优异配套系统PyQt5可视化检测交互界面二、数据集应用场景林业病虫害大范围普查山区、成片林区无人机航拍巡检自动识别松材线虫病枯死松树替代人工上山逐棵排查大幅降低野外巡检人力成本。森林疫点定位与溯源治理精准输出染病树木坐标位置林业部门划定疫区范围及时砍伐清理病树阻断松墨天牛媒介传播遏制病害扩散。时序林区长势监测多季度航拍数据对比追踪病害蔓延速度、区域变化为森林防疫规划、抚育方案提供数据依据。嵌入式机载边缘部署检测YOLOv11-n轻量化模型可搭载无人机机载端飞行过程实时回传病树检测结果实现边飞边检测。学术算法研究训练林区复杂背景植被交错、光影不均、地形起伏下小目标病害树检测算法研发、轻量化模型优化、航拍大图拼接检测研究。三、关键词标签中文关键词松材线虫病无人机航拍YOLOv11病树检测林区巡检目标检测PyQt5可视化森林病虫害航拍大图切片轻量化模型英文关键词Pine wilt disease; UAV aerial imagery; YOLOv11; diseased pine tree detection; forest patrol; object detection; PyQt5 GUI; forest pest control; large aerial image slicing; lightweight model四、完整代码1. YOLOv11-n 模型训练代码fromultralyticsimportYOLO# 1. 加载YOLOv11-n轻量化预训练权重modelYOLO(yolo11n.pt)# 2. 训练参数配置train_resultsmodel.train(datapine_wilt.yaml,# 数据集配置文件路径epochs100,# 训练轮数imgsz640,# 输入图像尺寸640*640batch16,# 批次大小device0,# 使用GPU训练无GPU改为cpupatience15,# 早停轮数lr00.001,# 初始学习率warmup_epochs3,# 预热轮数mosaic1.0,# 马赛克数据增强mixup0.1,saveTrue,valTrue,projectpine_disease_result,nameyolo11n_train)# 3. 训练结束评估模型metricsmodel.val()print(fmAP0.5:{metrics.box.map50:.3f})# 4. 单张图片推理测试model(test_pine.jpg,saveTrue)配套 pine_wilt.yaml 数据集配置文件# 数据集路径path:./pine_datasettrain:images/trainval:images/val# 类别信息names:0:unhealthy2. PyQt5 可视化检测界面完整代码对应你截图界面功能图片导入、视频/摄像头调用、实时检测、置信度筛选、坐标展示、结果表格展示、保存结果importsysimportcv2importnumpyasnpfromPyQt5.QtWidgetsimport(QApplication,QMainWindow,QWidget,QPushButton,QLabel,QFileDialog,QVBoxLayout,QHBoxLayout,QTableWidget,QTableWidgetItem,QDoubleSpinBox,QComboBox)fromPyQt5.QtGuiimportQImage,QPixmapfromPyQt5.QtCoreimportQt,QTimerfromultralyticsimportYOLOclassPineDetectUI(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(基于YOLOv11的航拍松材线虫病树检测系统)self.resize(1350,920)# 加载训练好的权重self.modelYOLO(./pine_disease_result/yolo11n_train/weights/best.pt)self.img_pathNoneself.current_imgNoneself.timerQTimer()self.cam_flagFalseself.conf_threshold0.8355self.init_ui()definit_ui(self):central_widgetQWidget()self.setCentralWidget(central_widget)main_layoutQHBoxLayout(central_widget)# 左侧图片显示区域left_layoutQVBoxLayout()self.img_labelQLabel(检测画面)self.img_label.setFixedSize(780,620)self.img_label.setStyleSheet(border:1px solid #aaa;)self.img_label.setAlignment(Qt.AlignCenter)left_layout.addWidget(self.img_label)# 下方结果表格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])left_layout.addWidget(self.result_table)# 右侧控制面板right_layoutQVBoxLayout()right_layout.setSpacing(15)# 文件导入区self.btn_imgQPushButton(文件导入)self.btn_img.clicked.connect(self.load_image)self.btn_videoQPushButton(请选择视频文件)self.btn_camQPushButton(摄像头未开启)self.btn_cam.clicked.connect(self.open_camera)right_layout.addWidget(self.btn_img)right_layout.addWidget(self.btn_video)right_layout.addWidget(self.btn_cam)# 检测结果参数right_layout.addWidget(QLabel(检测结果))self.label_timeQLabel(用时: 0.00 s)self.label_numQLabel(目标数目: 0)right_layout.addWidget(self.label_time)right_layout.addWidget(self.label_num)# 目标选择lay_selectQHBoxLayout()lay_select.addWidget(QLabel(目标选择:))self.combo_targetQComboBox()self.combo_target.addItems([全部,unhealthy])lay_select.addWidget(self.combo_target)right_layout.addLayout(lay_select)# 置信度lay_confQHBoxLayout()lay_conf.addWidget(QLabel(置信度))self.conf_spinQDoubleSpinBox()self.conf_spin.setRange(0,1)self.conf_spin.setValue(0.8355)self.conf_spin.setSingleStep(0.01)lay_conf.addWidget(self.conf_spin)right_layout.addLayout(lay_conf)# 坐标展示right_layout.addWidget(QLabel(目标位置))self.label_xminQLabel(xmin: 0)self.label_yminQLabel(ymin: 0)self.label_xmaxQLabel(xmax: 0)self.label_ymaxQLabel(ymax: 0)right_layout.addWidget(self.label_xmin)right_layout.addWidget(self.label_ymin)right_layout.addWidget(self.label_xmax)right_layout.addWidget(self.label_ymax)# 操作按钮lay_btnQHBoxLayout()self.btn_saveQPushButton(保存)self.btn_exitQPushButton(退出)self.btn_exit.clicked.connect(self.close)lay_btn.addWidget(self.btn_save)lay_btn.addWidget(self.btn_exit)right_layout.addLayout(lay_btn)main_layout.addLayout(left_layout)main_layout.addLayout(right_layout)defload_image(self):file,_QFileDialog.getOpenFileName(self,选择航拍图片,,图片(*.jpg *.png *.jpeg))iffile:self.img_pathfileself.detect_image()defdetect_image(self):importtime t_starttime.time()self.conf_thresholdself.conf_spin.value()# 推理resself.model(self.img_path,confself.conf_threshold)[0]t_costtime.time()-t_start# 绘制检测框imgres.plot()self.current_imgimg h,w,cimg.shape bytes_per_linec*w qimgQImage(img.data,w,h,bytes_per_line,QImage.Format_RGB888)self.img_label.setPixmap(QPixmap.fromImage(qimg).scaled(self.img_label.size(),Qt.KeepAspectRatio))# 刷新信息obj_numlen(res.boxes)self.label_time.setText(f用时:{t_cost:.3f}s)self.label_num.setText(f目标数目:{obj_num})# 填充表格self.result_table.setRowCount(obj_num)foridx,boxinenumerate(res.boxes):conffloat(box.conf)clsself.model.names[int(box.cls)]xyxybox.xyxy[0].cpu().numpy().astype(int)x1,y1,x2,y2xyxy pos_strf[{x1},{y1},{x2},{y2}]self.result_table.setItem(idx,0,QTableWidgetItem(str(idx1)))self.result_table.setItem(idx,1,QTableWidgetItem(self.img_path))self.result_table.setItem(idx,2,QTableWidgetItem(cls))self.result_table.setItem(idx,3,QTableWidgetItem(f{conf:.1f}))self.result_table.setItem(idx,4,QTableWidgetItem(pos_str))# 选中首个目标展示坐标ifidx0:self.label_xmin.setText(fxmin:{x1})self.label_ymin.setText(fymin:{y1})self.label_xmax.setText(fxmax:{x2})self.label_ymax.setText(fymax:{y2})defopen_camera(self):ifnotself.cam_flag:self.capcv2.VideoCapture(0)self.cam_flagTrueself.btn_cam.setText(摄像头开启中)self.timer.timeout.connect(self.camera_frame)self.timer.start(30)else:self.timer.stop()self.cap.release()self.cam_flagFalseself.btn_cam.setText(摄像头未开启)defcamera_frame(self):ret,frameself.cap.read()ifret:resself.model(frame,confself.conf_spin.value())[0]frame_drawres.plot()h,w,cframe_draw.shape bytes_per_linec*w qimgQImage(frame_draw.data,w,h,bytes_per_line,QImage.Format_RGB888)self.img_label.setPixmap(QPixmap.fromImage(qimg).scaled(self.img_label.size(),Qt.KeepAspectRatio))defcloseEvent(self,event):ifself.cam_flag:self.timer.stop()self.cap.release()event.accept()if__name____main__:appQApplication(sys.argv)windowPineDetectUI()window.show()sys.exit(app.exec_())3. 大图切片预处理代码50004000原图裁切640640importosfromPILimportImagedefslice_large_image(img_path,save_dir,slice_size640,overlap100):imgImage.open(img_path)w,himg.size os.makedirs(save_dir,exist_okTrue)x_stepslist(range(0,w,slice_size-overlap))y_stepslist(range(0,h,slice_size-overlap))foryiny_steps:forxinx_steps:x2min(xslice_size,w)y2min(yslice_size,h)crop_imgimg.crop((x,y,x2,y2))# 不足640填充黑边new_imgImage.new(RGB,(slice_size,slice_size),(0,0,0))new_img.paste(crop_img,(0,0))save_namef{os.path.basename(img_path)[:-4]}_{x}_{y}.jpgnew_img.save(os.path.join(save_dir,save_name))# 批量执行if__name____main__:raw_img_dir./raw_large_imagesslice_out./slice_640_imgsforpicinos.listdir(raw_img_dir):ifpic.endswith((jpg,png)):slice_large_image(os.path.join(raw_img_dir,pic),slice_out)补充说明环境依赖安装命令pipinstallultralytics pyqt5 opencv-python pillow numpyP-R曲线由YOLO训练结束后自动生成存储在runs/val/xxx/PR_curve.png大图切片加入重叠区域规避病树被切分导致漏检问题