Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning

TL;DR

This paper introduces a computer vision and deep learning-based system for counting and identifying train wagons, achieving high accuracy with low processing requirements and cost-effective implementation.

Contribution

It presents a novel two-stage deep learning approach that outperforms RFID-based solutions in accuracy and cost, using minimal training data and low-end hardware.

Findings

100% accuracy in counting wagons

99.7% recognition rate for identification

Effective in real-world scenarios with damaged codes

Abstract

In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning. The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID), which are known to have high installation and maintenance costs. According to our experiments, our two-stage methodology achieves impressive results on real-world scenarios, i.e., 100% accuracy in the counting stage and 99.7% recognition rate in the identification one. Moreover, the system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes. The results achieved were surprising considering that the proposed system requires low processing power (i.e., it can run in low-end setups) and that we used a relatively small number of images to train our Convolutional Neural Network (CNN) for character recognition. The proposed method is registered, under number BR512020000808-9, with the National Institute of Industrial Property (Brazil).