Search Results for author: Yoshihide Sekimoto

Found 7 papers, 3 papers with code

Road Rutting Detection using Deep Learning on Images

no code implementations28 Sep 2022 Poonam Kumari Saha, Deeksha Arya, Ashutosh Kumar, Hiroya Maeda, Yoshihide Sekimoto

The proposed road rutting dataset and the results of our research study will help accelerate the research on detection of road rutting using deep learning.

object-detection Object Detection +2

RDD2022: A multi-national image dataset for automatic Road Damage Detection

1 code implementation18 Sep 2022 Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Yoshihide Sekimoto

The data article describes the Road Damage Dataset, RDD2022, which comprises 47, 420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China.

object-detection Object Detection +1

Transfer Learning-based Road Damage Detection for Multiple Countries

1 code implementation30 Aug 2020 Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Alexander Mraz, Takehiro Kashiyama, Yoshihide Sekimoto

Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification.

Road Damage Detection Transfer Learning

City2City: Translating Place Representations across Cities

no code implementations26 Nov 2019 Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Satish V. Ukkusuri

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges.

Translation Word Embeddings

Congestion Analysis of Convolutional Neural Network-Based Pedestrian Counting Methods on Helicopter Footage

no code implementations5 Nov 2019 Gergely Csönde, Yoshihide Sekimoto, Takehiro Kashiyama

In this paper, we investigate state-of-the-art methods for counting pedestrians and the related performance of aerial footage.

Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone

2 code implementations29 Jan 2018 Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto, Takehiro Kashiyama, Hiroshi Omata

This dataset is composed of 9, 053 road damage images captured with a smartphone installed on a car, with 15, 435 instances of road surface damage included in these road images.

object-detection Object Detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.