Title: Real-time system for detecting high-emission diesel vehicles using deep learning

Authors: Yu-Yu Yen; Chih-Chun Chiu; Pin-Hao Huang; Jui-Hung Kao

Addresses: Department of Information Management, Shih Hsin University, No. 1, Ln. 17, Sec. 1, Muzha Rd., Wenshan Dist., Taipei City, 116, Taiwan, ROC ' Department of Information Management, Shih Hsin University, No. 1, Ln. 17, Sec. 1, Muzha Rd., Wenshan Dist., Taipei City, 116, Taiwan, ROC ' Department of Information Management, Shih Hsin University, No. 1, Ln. 17, Sec. 1, Muzha Rd., Wenshan Dist., Taipei City, 116, Taiwan, ROC ' Department of Information Management, Shih Hsin University, No. 1, Ln. 17, Sec. 1, Muzha Rd., Wenshan Dist., Taipei City, 116, Taiwan, ROC

Abstract: In recent years, air pollution emission statistics in Taiwan have indicated that mobile sources contribute the largest share of pollution in metropolitan areas. As a result, developing an intelligent, fully automated system for identifying high-emission diesel vehicles has become a critical necessity. The development of a high-pollution vehicle detection system utilises a one-stage architecture. A YOLOv4-based neural network module has been implemented to extend its application. The real-time image data of each vehicle is processed and optimally integrated through data augmentation, algorithm optimisation, and image enhancement. This enables the system to effectively identify high-pollution vehicles from complex, real-time traffic flow imagery and capture relevant vehicle information. Using these inputs, the system demonstrates enhanced adaptability to diverse environments. Furthermore, the system uses multiple image datasets for augmentation, incrementally improving accuracy. The simulation results indicate that the system achieves a detection accuracy that exceeds 91%, particularly for high-polluting diesel vehicles.

Keywords: deep learning; object detection; diesel vehicle pollution detection; high pollution emission monitoring system.

DOI: 10.1504/IJWGS.2025.150173

International Journal of Web and Grid Services, 2025 Vol.21 No.3/4, pp.248 - 268

Received: 30 Oct 2024
Accepted: 04 Jul 2025
Published online: 02 Dec 2025 *

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