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. 2024 Aug 13:10:e2233.
doi: 10.7717/peerj-cs.2233. eCollection 2024.

Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8

Caixia Lv  1 , Usha Mittal  2 , Vishu Madaan  2 , Prateek Agrawal  2   3
Affiliations

Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8

Caixia Lv et al. PeerJ Comput Sci. .

Abstract

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.

Keywords: Computer vision; Deep learning; Intelligent traffic management; Intelligent transport; Object detection; Smart city; Sustainable infrastructure; Sustainable transport; Thermal imaging; Vehicle detection.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Object detection example.
The raw image was obtained from the FLIR dataset. FLIR (https://www.flir.com/oem/adas/adas-dataset-agree/) allows anyone to use this dataset for non-commercial research and academic purposes.
Figure 2
Figure 2. Proposed methodology.
Figure 3
Figure 3. Architecture of EfficientDet.
Raw input image source: FLIR dataset.
Figure 4
Figure 4. Components of YOLOv8.
Figure 5
Figure 5. Flow diagram of proposed ensemble model.
Figure 6
Figure 6. Precision vs recall curve of EfficientDet, YOLOv8 and the proposed model on FLIR thermal dataset.
Figure 7
Figure 7. Precision vs recall curve of EfficientDet, YOLOv8 and proposed model on the FLIR RGB dataset.
Figure 8
Figure 8. mAP based performance comparison of EfficientDet, YOLOv8 and proposed ensemble based on mAP.
Figure 9
Figure 9. Detection made by proposed system.
Raw input image source: FLIR dataset.
See this image and copyright information in PMC

References

    1. Al Mudawi N, Qureshi AM, Abdelhaq M, Alshahrani A, Alazeb A, Alonazi M, Algarni A. Vehicle detection and classification via YOLOv8 and deep belief network over aerial image sequences. Sustainability. 2023;15(19):14597. doi: 10.3390/su151914597. - DOI
    1. Berg A, Ahlberg J, Felsberg M. A thermal object tracking benchmark. 2015 12th IEEE international conference on advanced video and signal based surveillance (AVSS); Piscataway. 2015.
    1. Byun S, Shin I-K, Moon J, Kang J, Choi S-I. Road traffic monitoring from UAV images using deep learning networks. Remote Sensing. 2021;13(20):4027. doi: 10.3390/rs13204027. - DOI
    1. Chan Y-M, Huang S-S, Fu L-C, Hsiao P-Y, Lo M-F. Vehicle detection and tracking under various lighting conditions using a particle filter. IET Intelligent Transport Systems. 2012;6(1):1. doi: 10.1049/iet-its.2011.0019. - DOI
    1. Chen Y-L, Chen T-S, Huang T-W, Yin L-C, Wang S-Y, Chiueh T-C. Intelligent urban video surveillance system for automatic vehicle detection and tracking in clouds. 2013 IEEE 27th international conference on advanced information networking and applications (AINA); Piscataway. 2013.