Accuracy Improvement of Automatic Smoky Diesel Vehicle Detection Using YOLO Model, Matching, and Refinement.

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Abstract

The detection of smoky diesel vehicles is a key step in reducing air pollution from transportation. We propose a new method for identifying smoky vehicles that proceeds in three stages: (1) the detection of vehicle shapes, license plates, and smoke regions; (2) the implementation of the two matching techniques based on the smoke region-vehicle shape and smoke region-license plate relationships; and (3) the refinement of the smoke region detected. The first stage involves the evaluation of various You Only Look Once (YOLO) models to identify the best-fit model for object detection. YOLOv5s was the most effective, particularly for the smoke region prediction, achieving a precision of 91.4% and a mean average precision at 0.5 ([email protected]) of 91%. It also had the highest mean [email protected] of 93.9% across all three classes. The application of the two matching techniques significantly reduced the rate of false negatives and enhanced the rate of true positives for the smoky diesel vehicles through the detection of their license plates. Moreover, a refinement process based on image processing theory was implemented, effectively eliminating incorrect smoke region predictions caused by vehicle shadows. As a result, our method achieved a detection rate of 97.45% and a precision of 93.50%, which are higher than that of the two existing popular methods, and produced an acceptable false alarm rate of 5.44%. Particularly, the proposed method substantially reduced the processing time to as low as 85 ms per image, compared to 140.3 and 182.6 ms per image in the two reference studies. In conclusion, the proposed method showed remarkable improvements in the accuracy, robustness, and feasibility of smoky diesel vehicle detection. Therefore, it offers potential to be applied in real-world situations.