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Artificial Intelligence Application in Automated Odometer Mileage Recognition of Freight Vehicles
Author(s)
Date Issued
January 1, 2021
Type
Conference Paper
Abstract
Transportation cost management is necessary for entrepreneurs in industry and business. One way to do this is to report the daily mileage numbers read by the employees of a company, but still encountering errors in human mileage reading, resulting in incorrect information received and difficulties to plan effective revenue management. As well as, increasing workload and creating complications for employees in checking mileage information. Therefore, the objective of this research is to create a machine learning model for detecting and reading the mileage numbers 2 types of freight vehicles: Analog and Digital. It can be divided into 2 parts: 1) detect mileage is used to identify the position of the mileage in the image and cut only the mileage by removing the unrelated background from the image 2) detect numbers and reads miles. Both use object detection with the Faster-RCNN. The results show that to detect the position of the miles and cut only the number of miles to read the numbers correctly, 187 images from 220 test images, which is correct for the model, representing 85%. The results of the study achieve satisfactory performance that meet the requirements needed for real-life applications in the transportation and logistics industry.
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12951 LNCS, 487-496, 2021