glasslucas00 / Meter_GUI
☆23Updated 2 years ago
Related projects: ⓘ
- 指针式仪表读数python程序☆56Updated last year
- 基于opencv的指针式仪表的识别与读数☆108Updated 5 years ago
- 使用YOLOv5+DeepLabV3Plus实现仪表的检测、指针表盘分割和刻度读数识别☆34Updated 2 years ago
- Read the value of the pointer meter☆59Updated 3 years ago
- 仪表盘刻度识别☆22Updated 3 years ago
- 分别使用OpenCV、ONNXRuntime部署yolov5检测车牌和4个角点,包含C++和Python两个版本的程序☆68Updated 2 years ago
- 使用ONNXRuntime部署百度飞桨开源PP-Vehicle车辆分析,包含车辆检测,识别车型和车辆颜色,车牌检测,车牌识别5个功能,不依赖PaddlePaddle就能运行,包含C++和Python两个版本的程序☆42Updated last year
- Use yolov5 to detect pointer scale☆18Updated last year
- Some changes on original repo☆25Updated last year
- ☆22Updated 4 years ago
- 多个网络摄像头进行拉流以及对象检测。平台使用Jetson TX2 (ARM architecture),海康摄像头,对象检测采用YOLO v3模型。☆79Updated 4 years ago
- Using YoloV3 to detect pointer instruments and reading the number by Hough Transform☆41Updated 2 years ago
- yolov7 部署版本,后处理用python语言和C++语言形式进行改写,便于移植不同平台(caffe、onnx、tensorRT、RKNN、Horzion)。☆31Updated last year
- Convert the official paddleocr model to a deployable model on RK1126☆32Updated last year
- ☆31Updated 4 years ago
- 一种适合工业级应用的基于深度学习的实时人脸检测与识别算法的C++实现,仅仅只依赖opencv库☆47Updated 2 years ago
- 用opencv的dnn模块做yolov5目标检测,包含C++和Python两个版本的程序,优化后的☆114Updated 2 years ago
- ☆62Updated last month
- 在海思Hisilicon的Hi3516dv300芯片上,利用nnie和opencv库,简洁了官方yolov3用例中各种复杂的嵌套调用/复杂编译,提供了交叉编译后可成功上板部署运行的demo。☆36Updated 2 years ago
- 基于QT的缺陷检测系统,包括图像检测以及目标检测两个部分,支持ONNXRuntime加速☆30Updated 2 years ago
- 用opencv的dnn模块实现Yolo-Fastest的目标检测☆47Updated 3 years ago
- yolov5检测人脸和关键点,只依赖opencv库就可以运行,程序包含C++和Python两个版本的☆62Updated 3 years ago
- 使用ONNXRuntime部署PicoDet目标检测,包含C++和Python两个版本的程序☆26Updated 2 years ago
- VideoCapture封装,读取rtsp的实时帧☆66Updated 4 years ago
- Using depth neural network to read traditional meter.☆26Updated 4 years ago
- yolov10 瑞芯微 rknn 板端 C++部署,使用平台 rk3588。☆58Updated 2 months ago
- 在瑞芯微rockchip的AI芯片rv1109上,利用rknn和opencv库,修改了官方yolov3后处理部分代码Bug,交叉编译yolov3-demo示 例后可成功上板部署运行。☆33Updated 2 years ago
- ☆42Updated 2 years ago
- 烟雾 火焰检测☆50Updated 3 years ago
- This is the first released system towards complex meters` detection and recognition, which is implemented by computer vision techniques.☆114Updated last year