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基于YOLOv8深度学习的120种犬类检测与识别系统是一款功能强大的工具#xff0c;该系统利用YOLOv8深度学习框架#xff0c;通过21583张图片的训练#xff0c;实现了对120种犬类的精准检测与识别。
该系统基于Python与PyQt5开发#xff0c;具有简洁的UI界面该系统利用YOLOv8深度学习框架通过21583张图片的训练实现了对120种犬类的精准检测与识别。
该系统基于Python与PyQt5开发具有简洁的UI界面支持图片、视频以及摄像头三种方式进行目标检测并能够将检测结果进行保存。在检测过程中系统能够实时显示目标位置、目标总数、置信度以及用时等信息为用户提供直观、清晰的检测结果。
该系统在多个领域具有广泛的应用价值。在公共安全领域警方和安保人员可以利用它快速识别搜救犬、警犬以及潜在的威胁性狗类提高工作效率和响应速度。在宠物行业它有助于宠物店、兽医诊所和动物收容所更准确地记录和管理犬只信息提供更个性化的服务。此外它还可以用于城市管理中监控流浪狗的数量和分布处理公共卫生问题和安全风险以及牧场和农场中牧羊犬的精准管理等。
总之基于YOLOv8深度学习的120种犬类检测与识别系统是一款高效、准确、易用的工具它的出现将极大地推动犬类检测与识别技术的发展为多个领域带来便利和价值。
【效果展示】 【测试环境】
windows10 anaconda3python3.8 torch1.9.0cu111 ultralytics8.2.95
【模型可以检测出类别】
affenpinscher
afghan_hound
african_hunting_dog
airedale
american_staffordshire_terrier
appenzeller
australian_terrier
basenji
basset
beagle
bedlington_terrier
bernese_mountain_dog
black-and-tan_coonhound
blenheim_spaniel
bloodhound
bluetick
border_collie
border_terrier
borzoi
boston_bull
bouvier_des_flandres
boxer
brabancon_griffon
briard
brittany_spaniel
bull_mastiff
cairn
cardigan
chesapeake_bay_retriever
chihuahua
chow
clumber
cocker_spaniel
collie
curly-coated_retriever
dandie_dinmont
dhole
dingo
doberman
english_foxhound
english_setter
english_springer
entlebucher
eskimo_dog
flat-coated_retriever
french_bulldog
german_shepherd
german_short-haired_pointer
giant_schnauzer
golden_retriever
gordon_setter
great_dane
great_pyrenees
greater_swiss_mountain_dog
groenendael
ibizan_hound
irish_setter
irish_terrier
irish_water_spaniel
irish_wolfhound
italian_greyhound
japanese_spaniel
keeshond
kelpie
kerry_blue_terrier
komondor
kuvasz
labrador_retriever
lakeland_terrier
leonberg
lhasa
malamute
malinois
maltese_dog
mexican_hairless
miniature_pinscher
miniature_poodle
miniature_schnauzer
newfoundland
norfolk_terrier
norwegian_elkhound
norwich_terrier
old_english_sheepdog
otterhound
papillon
pekinese
pembroke
pomeranian
pug
redbone
rhodesian_ridgeback
rottweiler
saint_bernard
saluki
samoyed
schipperke
scotch_terrier
scottish_deerhound
sealyham_terrier
shetland_sheepdog
shih-tzu
siberian_husky
silky_terrier
soft-coated_wheaten_terrier
staffordshire_bullterrier
standard_poodle
standard_schnauzer
sussex_spaniel
tibetan_mastiff
tibetan_terrier
toy_poodle
toy_terrier
vizsla
walker_hound
weimaraner
welsh_springer_spaniel
west_highland_white_terrier
whippet
wire-haired_fox_terrier
yorkshire_terrier 【训练信息】
参数值训练集图片数18945验证集图片数1738训练map73.5%训练精度(Precision)69.8%训练召回率(Recall)67.9%
【部分实现源码】
class Ui_MainWindow(QtWidgets.QMainWindow):signal QtCore.pyqtSignal(str, str)def setupUi(self):self.setObjectName(MainWindow)self.resize(1280, 728)self.centralwidget QtWidgets.QWidget(self)self.centralwidget.setObjectName(centralwidget)self.weights_dir ./weightsself.picture QtWidgets.QLabel(self.centralwidget)self.picture.setGeometry(QtCore.QRect(260, 10, 1010, 630))self.picture.setStyleSheet(background:black)self.picture.setObjectName(picture)self.picture.setScaledContents(True)self.label_2 QtWidgets.QLabel(self.centralwidget)self.label_2.setGeometry(QtCore.QRect(10, 10, 81, 21))self.label_2.setObjectName(label_2)self.cb_weights QtWidgets.QComboBox(self.centralwidget)self.cb_weights.setGeometry(QtCore.QRect(10, 40, 241, 21))self.cb_weights.setObjectName(cb_weights)self.cb_weights.currentIndexChanged.connect(self.cb_weights_changed)self.label_3 QtWidgets.QLabel(self.centralwidget)self.label_3.setGeometry(QtCore.QRect(10, 70, 72, 21))self.label_3.setObjectName(label_3)self.hs_conf QtWidgets.QSlider(self.centralwidget)self.hs_conf.setGeometry(QtCore.QRect(10, 100, 181, 22))self.hs_conf.setProperty(value, 25)self.hs_conf.setOrientation(QtCore.Qt.Horizontal)self.hs_conf.setObjectName(hs_conf)self.hs_conf.valueChanged.connect(self.conf_change)self.dsb_conf QtWidgets.QDoubleSpinBox(self.centralwidget)self.dsb_conf.setGeometry(QtCore.QRect(200, 100, 51, 22))self.dsb_conf.setMaximum(1.0)self.dsb_conf.setSingleStep(0.01)self.dsb_conf.setProperty(value, 0.25)self.dsb_conf.setObjectName(dsb_conf)self.dsb_conf.valueChanged.connect(self.dsb_conf_change)self.dsb_iou QtWidgets.QDoubleSpinBox(self.centralwidget)self.dsb_iou.setGeometry(QtCore.QRect(200, 160, 51, 22))self.dsb_iou.setMaximum(1.0)self.dsb_iou.setSingleStep(0.01)self.dsb_iou.setProperty(value, 0.45)self.dsb_iou.setObjectName(dsb_iou)self.dsb_iou.valueChanged.connect(self.dsb_iou_change)self.hs_iou QtWidgets.QSlider(self.centralwidget)self.hs_iou.setGeometry(QtCore.QRect(10, 160, 181, 22))self.hs_iou.setProperty(value, 45)self.hs_iou.setOrientation(QtCore.Qt.Horizontal)self.hs_iou.setObjectName(hs_iou)self.hs_iou.valueChanged.connect(self.iou_change)self.label_4 QtWidgets.QLabel(self.centralwidget)self.label_4.setGeometry(QtCore.QRect(10, 130, 72, 21))self.label_4.setObjectName(label_4)self.label_5 QtWidgets.QLabel(self.centralwidget)self.label_5.setGeometry(QtCore.QRect(10, 210, 72, 21))self.label_5.setObjectName(label_5)self.le_res QtWidgets.QTextEdit(self.centralwidget)self.le_res.setGeometry(QtCore.QRect(10, 240, 241, 400))self.le_res.setObjectName(le_res)self.setCentralWidget(self.centralwidget)self.menubar QtWidgets.QMenuBar(self)self.menubar.setGeometry(QtCore.QRect(0, 0, 1110, 30))self.menubar.setObjectName(menubar)self.setMenuBar(self.menubar)self.statusbar QtWidgets.QStatusBar(self)self.statusbar.setObjectName(statusbar)self.setStatusBar(self.statusbar)self.toolBar QtWidgets.QToolBar(self)self.toolBar.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon)self.toolBar.setObjectName(toolBar)self.addToolBar(QtCore.Qt.TopToolBarArea, self.toolBar)self.actionopenpic QtWidgets.QAction(self)icon QtGui.QIcon()icon.addPixmap(QtGui.QPixmap(:/images/1.png), QtGui.QIcon.Normal, QtGui.QIcon.Off)self.actionopenpic.setIcon(icon)self.actionopenpic.setObjectName(actionopenpic)self.actionopenpic.triggered.connect(self.open_image)self.action QtWidgets.QAction(self)icon1 QtGui.QIcon()icon1.addPixmap(QtGui.QPixmap(:/images/2.png), QtGui.QIcon.Normal, QtGui.QIcon.Off)self.action.setIcon(icon1)self.action.setObjectName(action)self.action.triggered.connect(self.open_video)self.action_2 QtWidgets.QAction(self)icon2 QtGui.QIcon()icon2.addPixmap(QtGui.QPixmap(:/images/3.png), QtGui.QIcon.Normal, QtGui.QIcon.Off)self.action_2.setIcon(icon2)self.action_2.setObjectName(action_2)self.action_2.triggered.connect(self.open_camera)self.actionexit QtWidgets.QAction(self)icon3 QtGui.QIcon()icon3.addPixmap(QtGui.QPixmap(:/images/4.png), QtGui.QIcon.Normal, QtGui.QIcon.Off)self.actionexit.setIcon(icon3)self.actionexit.setObjectName(actionexit)self.actionexit.triggered.connect(self.exit)self.toolBar.addAction(self.actionopenpic)self.toolBar.addAction(self.action)self.toolBar.addAction(self.action_2)self.toolBar.addAction(self.actionexit)self.retranslateUi()QtCore.QMetaObject.connectSlotsByName(self)self.init_all()
【使用步骤】
使用步骤 1首先根据官方框架https://github.com/ultralytics/ultralytics安装教程安装好yolov8环境并安装好pyqt5 2切换到自己安装的yolov8环境后并切换到源码目录执行python main.py即可运行启动界面进行相应的操作即可
【提供文件】
python源码 yolov8s.onnx模型不提供pytorch模型 训练的map,P,R曲线图(在weights\results.png) 测试图片在test_img文件夹下面
【源码下载地址】
https://download.csdn.net/download/FL1623863129/89831387
