Universal robust vehicle identification system
With the exponential rise in vehicular traffic volume, an intelligent system that is able to detect and classify would be essential. Image processing has already placed its significance for real-world applications on machine learning, one of which is traffic analysis. Through this study, the researc...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_ece/20 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1022&context=etdb_ece |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | With the exponential rise in vehicular traffic volume, an intelligent system that is able to detect and classify would be essential. Image processing has already placed its significance for real-world applications on machine learning, one of which is traffic analysis. Through this study, the researchers developed a system that allows vehicular tracking and identification using the methods of neural networks for object detection, specifically the YOLOv5 algorithm. The process involved system training and testing programmed in the Python environment. The system training utilized an initial dataset generated by the researchers that eliminated the manual intervention of image annotation. The developed system was intervened under the conditions of two different locations. It showed its detection and recognition capabilities through the integrated system features, namely, counting, vehicle type classification, and traffic condition assessment. A region of interest (ROI) line was the primary basis for counting, and a lane-per-lane evaluation was displayed. These features were analyzed individually, between the actual and detected and wrong and correct detections. On the features of vehicle class and type identification, and detection count, the Video 1, Video 2 and Video 3 got an accuracy percentage of 83%, 90% and 90%, respectively, for vehicle classification, 94%,100%, and 97%, respectively for type of vehicle, 81%, 82% and 82%, respectively for color and 93%, 93% and 97% for detection count, respectively. The increasing trend on accuracy proves the efficiency of the fine-tuning process implemented on the system implementation except for the color that requires a separate training process. After the last finetuning process, an accuracy of 92.57% for vehicle class, 93% for type identification, 82% for |
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