论文标题
根据计算机视觉和深度学习的自动计数和识别火车货车
Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning
论文作者
论文摘要
在这项工作中，我们提出了一种强大而有效的解决方案，用于使用计算机视觉和深度学习来计算和识别火车货车。提出的解决方案具有成本效益，可以轻松地基于射频识别（RFID）替代解决方案，该解决方案已知具有较高的安装和维护成本。根据我们的实验，我们的两阶段方法在现实世界情景上取得了令人印象深刻的结果，即计数阶段的精度为100％，在识别阶段的识别率为99.7％。此外，由于识别代码损坏，该系统能够自动拒绝一些成功计数的火车货车。考虑到所提出的系统需要较低的处理能力（即，它可以在低端设置中运行），并且我们使用相对少量的图像来训练我们的卷积神经网络（CNN）以识别角色识别，这是令人惊讶的。提出的方法是根据国立工业产业研究所（巴西）注册的BR5120200808-9。
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).
