Pinned Repositories
BEVFusion-ROS-TensorRT
BEVFusion-ROS-TensorRT-CPP real time inference including ros1 & ros2.
darknet_ros2
darknet YOLOv4-FP16 + ROS2 Foxy
hello-algo
《Hello 算法》：动画图解、一键运行的数据结构与算法教程，支持 Java, C++, Python, Go, JS, TS, C#, Swift, Rust, Dart, Zig 等语言。
Lidar_AI_Solution
A project demonstrating Lidar related AI solutions, including three GPU accelerated Lidar/camera DL networks (PointPillars, CenterPoint, BEVFusion) and the related libs (cuPCL, 3D SparseConvolution, YUV2RGB, cuOSD,).
lidar_curb_detection
3D Lidar Curb Dectection implementation in ROS
Object-Grasp-Detection-ROS
Real-time Object Grasp Detection ROS package for YOLO
occupancy_bevdet
Picture-defogging
利用暗通道去雾法恢复图片质量
ros1_traffic_light_detect
用于ros1的交通信号灯识别
yolov5s_trt_ros
利用TensorRT对yolov5s进行加速，并将其应用于ROS，实现交通标志、红绿灯(直接输出路灯状态)、行人和车辆等交通场景的检测。
wk123467's Repositories
wk123467/yolov5s_trt_ros
利用TensorRT对yolov5s进行加速，并将其应用于ROS，实现交通标志、红绿灯(直接输出路灯状态)、行人和车辆等交通场景的检测。
wk123467/BEVFusion-ROS-TensorRT
BEVFusion-ROS-TensorRT-CPP real time inference including ros1 & ros2.
wk123467/darknet_ros2
darknet YOLOv4-FP16 + ROS2 Foxy
wk123467/hello-algo
《Hello 算法》：动画图解、一键运行的数据结构与算法教程，支持 Java, C++, Python, Go, JS, TS, C#, Swift, Rust, Dart, Zig 等语言。
wk123467/Lidar_AI_Solution
A project demonstrating Lidar related AI solutions, including three GPU accelerated Lidar/camera DL networks (PointPillars, CenterPoint, BEVFusion) and the related libs (cuPCL, 3D SparseConvolution, YUV2RGB, cuOSD,).
wk123467/lidar_curb_detection
3D Lidar Curb Dectection implementation in ROS
wk123467/Object-Grasp-Detection-ROS
Real-time Object Grasp Detection ROS package for YOLO
wk123467/occupancy_bevdet
wk123467/Picture-defogging
利用暗通道去雾法恢复图片质量
wk123467/ros1_traffic_light_detect
用于ros1的交通信号灯识别
wk123467/daily_morning
给别人家的女朋友发早安
wk123467/time-push
for love
wk123467/Traffic-signal-recognition
对交通信号进行检测、识别
wk123467/wechat-public-account-push
微信公众号推送-给女朋友的浪漫
wk123467/wk123467
wk123467/wx-push
关于公众号推送
wk123467/YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~