分别使用OpenCV、ONNXRuntime部署YOLOPV2目标检测+可驾驶区域分割+车道线分割,一共包含54个onnx模型,依然是包含C++和Python两个版本的程序。仅仅只依赖OpenCV就能运行,彻底摆脱对任何深度学习框架的依赖。
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Updated
Sep 20, 2022 - C++
分别使用OpenCV、ONNXRuntime部署YOLOPV2目标检测+可驾驶区域分割+车道线分割,一共包含54个onnx模型,依然是包含C++和Python两个版本的程序。仅仅只依赖OpenCV就能运行,彻底摆脱对任何深度学习框架的依赖。
使用OpenCV部署HybridNets,同时处理车辆检测、可驾驶区域分割、车道线分割,三项视觉感知任务,包含C++和Python两种版本的程序实现。本套程序只依赖opencv库就可以运行, 彻底摆脱对任何深度学习框架的依赖。
Perform inference with TwinLiteNet model using ONNX Runtime. TwinLiteNet is a lightweight and efficient deep learning model designed for drivable area and lane segmentation
An easy-to-use implementation for performing inferencing with TwinLiteNet model using OpenCV DNN module. TwinLiteNet is a lightweight and efficient deep learning model designed for drivable area and lane segmentation
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