文献类型：期刊文献

英文题名：Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8

作者：Lv, Caixia[1];Mittal, Usha[2];Madaan, Vishu[2];Agrawal, Prateek[2,3]

第一作者：Lv, Caixia

通讯作者：Agrawal, P[1];Agrawal, P[2]

机构：[1]Beijing Union Univ, Smart City Coll, Beijing, Peoples R China;[2]Lovely Profess Univ, Comp Sci & Engn, Phagwara, Punjab, India;[3]Shree Guru Gobind Singh Tricentenary Univ, Gurugram, Haryana, India

第一机构：北京联合大学继续教育学院

通讯机构：[1]corresponding author), Lovely Profess Univ, Comp Sci & Engn, Phagwara, Punjab, India;[2]corresponding author), Shree Guru Gobind Singh Tricentenary Univ, Gurugram, Haryana, India.

年份：2024

卷号：10

外文期刊名：PEERJ COMPUTER SCIENCE

收录：;EI(收录号：20243717018376);Scopus(收录号：2-s2.0-85203271422);WOS:【SCI-EXPANDED(收录号:WOS:001294141700003)】；

语种：英文

外文关键词：Deep learning; Smart city; Sustainable infrastructure; Computer vision; Object detection; Intelligent transport; Sustainable transport; Intelligent traffic management; Thermal imaging; Vehicle detection

摘要：With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models i.e., EfficientDet and YOLOv8. The proposed work leverages data from the Forward-Looking Infrared (FLIR) dataset, which provides both thermal and RGB images. To enhance the model performance and to address the class imbalances, we applied several data augmentation techniques. Experimental results demonstrate that the proposed ensemble model achieves a mean average precision (mAP) of 95.5% on thermal images, outperforming the individual performances of EfficientDet and YOLOv8, which achieved mAPs of 92.6% and 89.4% respectively. Additionally, the ensemble model attained an average recall (AR) of 0.93 and an optimal localization recall precision (oLRP) of 0.08 on thermal images. For RGB images, the ensemble model achieved mAP of 93.1%, AR of 0.91, and oLRP of 0.10, consistently surpassing the performance of its constituent models. These findings highlight the effectiveness of proposed ensemble approach in improving vehicle detection and classification. The integration of thermal imaging further enhances detection capabilities under various lighting conditions, making the system robust for real-world applications in intelligent traffic management.