This project is part of my bachelor’s thesis.The goal is to get a gopigo car to detect lanes provided by the raspberry pi camera v2. Return an information about the direction of the lane and keep the lane with a p-controller. Additionally, I implemented an obstacle detection with a haar cascade for cars. Pipeline The workthrough of the lane detection and lane keeping is the following: A possibly distorted input image is provided by the raspberry pi camera. With the […]

Setup # Setup python virtual environment $ virtualenv venv –python=python3 $ source venv/bin/activate # change directory to the repo where we have requirements file $ cd Regular_Expressions/ # Install python dependencies $ pip3 install -r requirements.txt Run Use the following command in the current directory. python3 src/main.py data/dev/ output/dev.csv Data The assignment’s development data can be found inside data/dev. GitHub View Github    

The models Our model comprises two parts: scene text detection and scene text recognition. the descriptions of these two models are as follow: Scene Text Detection We employ YoloV5 [1] to detect the ROI (Region Of Interest) from an image and Resnet50 [2] to implement the ROI transformation algorithm. This algorithm transforms the coordinates detected by YoloV5 to the proper location, which fits the text well. YoloV5 can detect all ROIs that might be strings while ROI transformation can make […]

Detecting haze image with hazer. What is hazer Hazer is a lib for getting “haze degree”. This repository is python version of hazer:https://cran.r-project.org/web/packages/hazer/vignettes/detecting-hazy-images.htmlhttps://github.com/bnasr/hazer/ The method is from this paper: https://www.hilarispublisher.com/open-access/detecting-foggy-images-and-estimating-the-haze-degree-factor-jcsb.1000226.pdf How to use git clone https://github.com/Joey777210/hazer-py.git Run the example in hazer_example.py. GitHub View Github    

OpenCVを利用した機械学習データをもとに、VRChatのスクリーンショットなどからＹＫＫさん（もとい「幽狐族のお姉様」）を検出できるソフトウェアです。 マニュアル こちらから実行環境のセットアップから解説する詳細なマニュアルをご覧いただけます。 ライセンス Pythonバージョンについて Python 3.8未満のバージョンは(2.xとかでない限りおそらく動くと思いますが)サポートしておりません。 サードパーティのライブラリに関する表記 本ソフトウェアには が使用されております。 謝辞 ＹＫＫさんのサンプルデータ収集にご協力いただいた皆さん、並びに大量の画像データを提供してくださったseptem47さんにこの場を借りて感謝申し上げます。 GitHub View Github    

This project hosts the scripts for training SWA object detectors, as presented in our paper: @article{zhang2020swa, title={SWA Object Detection}, author={Zhang, Haoyang and Wang, Ying and Dayoub, Feras and S{“u}nderhauf, Niko}, journal={arXiv preprint arXiv:2012.12645}, year={2020} } The full paper is available at: https://arxiv.org/abs/2012.12645. Introduction Do you want to improve 1.0 AP for your object detector without any inference cost and any change to your detector? Let us tell you such a recipe. It is surprisingly simple: train your detector for an […]

Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. Probabilistic two-stage detection,Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl,arXiv technical report (arXiv 2103.07461) Contact: [email protected]. Any questions or discussions are welcomed! Abstract We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score […]

(ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. If our project is helpful for your research, please consider citing: @inproceedings{zhaomorph, author = {Xiangyun Zhao, Xu Zou, Ying Wu}, title = {Morphable Detector for Object Detection on Demand}, booktitle = {ICCV}, Year = {2021} } Install First, install PyTorch and torchvision. We have tested on version of 1.8.0 with CUDA 11.0, but the other versions should also be working. Our code is    

Project for the IJCAI 2021 paper “Structure Guided Lane Detection” Abstract Recently, lane detection has made great progresswith the rapid development of deep neural networksand autonomous driving. However, thereexist three mainly problems including characterizinglanes, modeling the structural relationship betweenscenes and lanes, and supporting more attributes(e.g., instance and type) of lanes. In thispaper, we propose a novel structure guided frameworkto solve these problems simultaneously. Inthe framework, we first introduce a new lane representationto characterize each instance. Then a topdownvanishing point guided […]