Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals

Verma, Ankit; Sharma, Monika; Hebbalaguppe, Ramya; Hassan, Ehtesham; Vig, Lovekesh

doi:10.1109/icmla.2016.0130

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…One of the limitations is that CNN cannot detect objects well when they change in size, rotate, or shift in space. In contrast, spatial transformer network (STN) can manipulate data spatially in the network [6][7][8][9]. It has been proposed to learn invariance to spatial changes such as size changes, rotations, and position translations.…”

Section: Related Work a Stn Based Video Predictionmentioning

confidence: 99%

Shape Based Deep Estimation of Future Plant Images

et al. 2022

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Plants exhibit dynamic changes as they grow. For example, a new leaf may appear suddenly, and rotate and fold over time. Therefore, it is difficult to predict the growth of plants. For accurate growth predictions, it is important to predict the shapes, colors and textures of the leaves. The conventional methods simply use RGB images to predict the next plant at once. In this paper, we propose a novel deep network which is divided into two subnets of shape estimation and color reconstruction. Four gray time-series images are first aligned to a future target using a spatial transformer network (STN) for shape estimation. They are then fused using U-Net with two LSTMs to generate a future shape image. The color reconstruction subnet fuses the predicted shape with a RGB plant image to restore the color information. In addition, we use gray images with texture information for shape estimation instead of binary images with only simple information and RGB images with too much information. The proposed deep network can robustly generate future plant images for plant growth prediction. It is evaluated using our proprietary dataset as well as two public datasets for different types of plants. The experimental results demonstrate that our proposed network predicts the leaf shape more accurately and restores RGB. As a result, our method can create accurate future plant images.

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Section: Related Work a Stn Based Video Predictionmentioning

confidence: 99%

Shape Based Deep Estimation of Future Plant Images

et al. 2022

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“…Although tools for the digitization of engineering drawings in industries are in high demand, this problem has received relatively little attention in the research community. Relatively few attempts have been made in the past to address digitization of complex engineering documents comprising of both textual and graphical elements, for example: complex receipts, inspection sheets, and engineering diagrams (Verma et al, 2016), (Wang et al, 2009), (Arroyo et al, 2014), (Gupta et al, 2017), (Adam et al, 2000). We have found that connected component analysis (Koo and Kim, 2013) is heavily employed for text-segmentation for such documents (Verma et al, 2016).…”

Section: Introductionmentioning

confidence: 97%

Automatic Information Extraction from Piping and Instrumentation Diagrams

Rahul

Paliwal

Sharma

et al. 2019

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods

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One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. These files need to be digitized for purposes of inventory management and updation, and easy reference to different components of the schematics. There are several challenging vision problems associated with digitizing real world P&ID diagrams. Real world P&IDs come in several different resolutions, and often contain noisy textual information. Extraction of instrumentation information from these diagrams involves accurate detection of symbols that frequently have minute visual differences between them. Identification of pipelines that may converge and diverge at different points in the image is a further cause for concern. Due to these reasons, to the best of our knowledge, no system has been proposed for end-to-end data extraction from P&ID diagrams. However, with the advent of deep learning and the spectacular successes it has achieved in vision, we hypothesized that it is now possible to re-examine this problem armed with the latest deep learning models.To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols. This is followed by association of the detected components with the appropriate pipeline. The extracted pipeline information is used to populate a tree-like data structure for capturing the structure of the piping schematics. We have also evaluated our proposed method on a real world dataset of P&ID sheets obtained from an oil firm and have obtained extremely promising results. To the best of our knowledge, this is the first system that performs end-to-end data extraction from P&ID diagrams.

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“…In rail transport, it is increasingly common the development of systems that enable the automatic counting and identification of wagons (also known as railway cars) during the passage of a train through a station or its entry into a depot [2][3][4][5][6]. Given its importance, this is a topic that has been addressed in the literature since the mid-1990s [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…This method provides fast and accurate results, however, installing extra hardware on each wagon increases both installation and maintenance costs considerably [9]. These high costs were also highlighted in the context of container identification (which is very similar to the identification of wagons) by Verma et al [2], who stated that although modern containers have spaces reserved for the installation of RFID readers, such readers are not used by any major ocean container carriers due to high costs.…”

Section: Introductionmentioning

confidence: 99%

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

Laroca¹,

Boslooper²,

Menotti³

2020

Preprint

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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).An article about the proposed system has been published in the October 2020 issue of Railway Gazette International [1], the leading business journal for the worldwide rail industry.

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Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals

Cited by 15 publications

References 16 publications

Shape Based Deep Estimation of Future Plant Images

Shape Based Deep Estimation of Future Plant Images

Automatic Information Extraction from Piping and Instrumentation Diagrams

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

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