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Autonomous Vehicles

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하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/VideoObjectSearch/ALFNet

https://github.com/Vijayabhaskar96/Object-Detection-Algorithms

YOLOv3: An Incremental Improvement

https://github.com/VinayBN8997/CIFAR-10-object-detection

Densely Connected Convolutional Networks

https://github.com/VinayBN8997/ResNet-CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/Vipermdl/E2E-ERFNet

End-to-End Lane Marker Detection via Row-wise Classification

https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet

Deep Residual Learning for Image Recognition

https://github.com/Viveksbawa/SARAS-ESAD-Baseline

Focal Loss for Dense Object Detection

https://github.com/VladimirYugay/Gaussian-SLAM

Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting

https://github.com/VladimirYugay/MAGiC-SLAM

MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM

https://github.com/VladyslavUsenko/basalt-headers-mirror

The Double Sphere Camera Model

https://github.com/WangMian-Maker/Flask

YOLOv3: An Incremental Improvement

https://github.com/WangWenhao0716/Adapted-Center-and-Scale-Prediction

Adapted Center and Scale Prediction: More Stable and More Accurate

https://github.com/Wangxy2180/darknetKinectDetect

Focal Loss for Dense Object Detection

https://github.com/WaterScenes/Awesome-Water-Surface-Perception

WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces

https://github.com/WhutChengjun/VI-ORB

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/Will-J-Gale/GTA5-Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/Winaway/Path_planning

On a Formal Model of Safe and Scalable Self-driving Cars

https://github.com/Wizaron/instance-segmentation-pytorch

Semantic Instance Segmentation with a Discriminative Loss Function

https://github.com/WongKinYiu/YOLO

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/WongKinYiu/yolor

2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection

https://github.com/XRONHH/text_1

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/XiangbingJi/Stanford-cs230-final-project

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/XiaoMi/mace

Fast Algorithms for Convolutional Neural Networks

https://github.com/XuLena/3D-sensing

nuScenes: A multimodal dataset for autonomous driving

https://github.com/XuanjieXiao/vehicle-detection-by-using-camera

YOLOv3: An Incremental Improvement

https://github.com/YCHung1998/Object-Detection

YOLOv3: An Incremental Improvement

https://github.com/YHKimGithub/FATSnet

Temporally smooth online action detection using cycle-consistent future anticipation

https://github.com/YIZHE12/fashiontags

Focal Loss for Dense Object Detection

https://github.com/YIZHE12/robots

Focal Loss for Dense Object Detection

https://github.com/YU1ut/Ensemble-of-Leave-out-Classifiers

Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers

https://github.com/YXu556/RoadExtraction

Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation

https://github.com/Yamahitsuji/darknet

YOLOv3: An Incremental Improvement

https://github.com/Yang-YiFan/DiracDeltaNet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/YaraAlnaggar/SqueezeSeg

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

https://github.com/Yashgh7076/Eluvio-ML-Scene-Segmentation

Focal Loss for Dense Object Detection

https://github.com/YeLyuUT/UAVidToolKit

UAVid: A Semantic Segmentation Dataset for UAV Imagery

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.