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Open AccessArticle

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

1,2, 1,*, 1, 1, 1,2, 1,2 and 1,2
1
Key Laboratory of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
2
University of Science and Technology of China, Hefei 230026, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3540; https://doi.org/10.3390/s19163540
Received: 25 June 2019 / Revised: 10 August 2019 / Accepted: 11 August 2019 / Published: 13 August 2019
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Abstract

Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS. View Full-Text
Keywords: optical remote sensing system; emission data analysis; self-adaptive clustering database; automatic high-emitting recognition optical remote sensing system; emission data analysis; self-adaptive clustering database; automatic high-emitting recognition
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Xie, H.; Zhang, Y.; He, Y.; You, K.; Fan, B.; Yu, D.; Li, M. Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System. Sensors 2019, 19, 3540.

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