音響による接近車両検出における長時間データを有効に扱えるCNNモデルの提案・評価

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2024/12/01

タイトル(英語): Proposal and Evaluation of a CNN Model Capable of Effectively Handling Long-time Data for Approaching Vehicle Detection Using Sound

著者名: 伊藤　隆佑（名城大学 大学院 理工学研究科），神谷　珠緒（名城大学 大学院 理工学研究科），旭　健作（名城大学 大学院 理工学研究科），坂野　秀樹（名城大学 大学院 理工学研究科）

著者名(英語): Ryusuke Ito (Guraduate School of Science and Technology, Meijo University), Tamao Kamiya (Guraduate School of Science and Technology, Meijo University), Kensaku Asahi (Guraduate School of Science and Technology, Meijo University), Hideki Banno (Guraduate School of Science and Technology, Meijo University)

キーワード: 音響分類，機械学習，CNN（畳み込みニューラルネットワーク），入力データ長　　acoustic classification，machine learning，CNN (Convolutional Neural Network)，input data length

要約(英語): In Japan, head-on collisions involving automobiles constitute approximately 30% of accidents between vehicles, placing them among the leading causes. Therefore, our research focuses on preventing head-on collisions by studying the detection of approaching vehicles using a Convolutional Neural Network (CNN) based on road environment sounds. To improve the detection accuracy of approaching vehicles using audio data, we believe it is desirable to handle longer-length input data. Therefore, we conducted verification of the impact on detection accuracy by varying the time length of input data to the conventional model. The results indicated that the conventional model may not effectively handle long-length data. Consequently, in this paper, we propose and evaluate a new CNN model that divides the input data at the central time point. As a result, the input data length of 2.49 seconds yielded the highest accuracy, which is 2.49 times longer than the conventional length. Additionally, the detection accuracy of approaching vehicles in the proposed model improved by about 5-10 percentage points compared to the accuracy of the conventional model.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.144 No.12 （2024） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1143-1152 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/144/12/144_1143/_article/-char/ja/