Pinned Repositories
2D-and-3D-face-alignment
aimet
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Auto-Depth
3D Reconstruction / Pseudo LiDAR via Deep Learning
autonomous-driving-vehicle-detection
Created vehicle detection and tracking pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
Cam2BEV
TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
CARLA-Lane_Detection
Carla-RL
Reinforcement Learning codebase for self-driving car in Carla
Codes-for-Steering-Control
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks (AAAI 2019, oral)
driving-lane-departure-warning
Built a real-time lane departure warning system with a monocular camera, using OpenCV.
SuperDrive
A live deep neural network based lane/path detection system based on SuperCombo
kaishijeng's Repositories
kaishijeng/SuperDrive
A live deep neural network based lane/path detection system based on SuperCombo
kaishijeng/autonomous-driving-vehicle-detection
Created vehicle detection and tracking pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
kaishijeng/Codes-for-Steering-Control
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks (AAAI 2019, oral)
kaishijeng/driving-lane-departure-warning
Built a real-time lane departure warning system with a monocular camera, using OpenCV.
kaishijeng/2D-and-3D-face-alignment
kaishijeng/aimet
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
kaishijeng/Auto-Depth
3D Reconstruction / Pseudo LiDAR via Deep Learning
kaishijeng/Cam2BEV
TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
kaishijeng/CARLA-Lane_Detection
kaishijeng/Carla-RL
Reinforcement Learning codebase for self-driving car in Carla
kaishijeng/CenterTrack
Simultaneous object detection and tracking using center points.
kaishijeng/darknet
YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
kaishijeng/DMPHN-cvpr19-master
Pytorch Implementation of CVPR19 "Deep Stacked Multi-patch Hierarchical Network for Image Deblurring"
kaishijeng/edgetpu-compiler-example
kaishijeng/HANet
Official PyTorch implementation of HANet (CVPR 2020)
kaishijeng/Lane_Detection-An_Instance_Segmentation_Approach
A PyTorch implementation of the paper《Towards End-to-End Lane Detection: an Instance Segmentation Approach》
kaishijeng/LaneDetection_End2End
End-to-end Lane Detection for Self-Driving Cars (ICCV 2019 Workshop)
kaishijeng/lanefinder
TPU accelerated traffic lane segmentation engine for your Raspberry Pi
kaishijeng/mega.pytorch
Memory Enhanced Global-Local Aggregation for Video Object Detection, CVPR2020
kaishijeng/modeld
Self driving car lane and path detection
kaishijeng/MyGitHubtest
kaishijeng/PolyLaneNet
Repository for the paper entitled "PolyLaneNet: Lane Estimation via Deep Polynomial Regression"
kaishijeng/PyNET
Generating RGB photos from RAW image files with PyNET
kaishijeng/pytorch-unflow
a reimplementation of UnFlow in PyTorch that matches the official TensorFlow version
kaishijeng/Road-Lane-Detection-Using-DCNN-CS-670-
Lane detection using Encoder-Decoder Model with ConvLSTM and CNNs
kaishijeng/ShelfNet
implementation for paper "ShelfNet for fast semantic segmentation"
kaishijeng/Synaptic-Flow
kaishijeng/tensorflow-yolov4-tflite
YOLOv4, YOLOv3, YOLO-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
kaishijeng/thermal-camera
Code for a Raspberry Pi-based multispectral camera. Captures images from a Raspberry Pi Camera V2 and a FLIR Lepton 3 and combines them.
kaishijeng/Vehicle_Collision_Prediction_Using_CNN-LSTMs
Predict Vehicle collision moments before it happens!. CNN and LSTM hybrid architecture is used to understand a series of images.