@article{ref1,
title="Improvement of pedestrian collision detection rate using deep learning with data augmentation",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2022",
author="Kunitomi, Shouhei and Sukegawa, Yoshihiro and Shirakawa, Masayuki",
volume="53",
number="2",
pages="391-396",
abstract="We previously performed pedestrian collision detection using a deep learning method based on dashcam video data. However, the detection accuracy was poor owing to insufficient training data. Herein, we attempted to improve the accuracy of the detection for Advanced Automatic Crash Notification System (AACN) using data augmentation, which increases the amount of data by adding artificially generated training data. As a result of comparing the effects of multiple image processing methods on the detection rate, the detection rate increased to 86.85% by adding training data with reduced contrast. This rate was 34.37 points higher than the conventional rate.   ===  既存の画像を加工し学習データを増加させるData Augmentationを用いることで先進事故自動通報システムの活用に向けた深層学習による歩行者衝突検知精度向上を試みた．その結果，コントラストを低下させた学習データを追加することで検出率が86.8％となり従来手法に対し34.3points上昇した．<p /> <p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.53.391",
url="http://dx.doi.org/10.11351/jsaeronbun.53.391"
}