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Computer vision & AI Rail crossing Photo
  • Athira M D
  • August 16, 2021

Computer Vision and AI-based Railroad Crossing Detection

In the last 40 years, more than 22,000 people were killed at U.S. railroad crossings*. But data shows that railroad crossings have become safer over time, thanks to the installation of gates and lights at more crossings, timed traffic signals and increased public education efforts. Helping further with these efforts are several noted companies and startups  which are using technology such as the adoption of computer vision applications based on Machine learning techniques and artificial neural networks to tackle this problem.  

This article describes AI powered railroad crossing detection, a critical application that is associated with improving track safety.

Fig 1: Railroad Crossing Detection, Source: Operation Lifesaver, a nonprofit focused on railroad crossing safety

RailRoad Crossings

A railroad intersection is where a roadway crosses railroad tracks at the same level. This term also applies when a rail line with a separate right-of-way or reserved track crosses a road. Other names for railroad intersections are railway level crossing, grade crossing, road through railroad, criss-cross, railroad crossing, train crossing etc. An overview of railroad intersections will be found here Railroad Intersection.

Image Source

Road Detection

Many methods are used for road, lane and rail detection, using both image processing [Image Processing on Road Detection] and deep learning based techniques [Road detection based on simultaneous deep learning approaches]. However, no single solution is mentioned to be applicable in this circumstance of mixed scenario of rail and road. So, one way to address this use case is to have a single technique, which can efficiently detect the rail portions in the railroad crossings.

Image Source

Data Preparation

Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves labelling data, reformatting data, making corrections to data and the combining of data sets to enrich data. In this case, the data is collected by dumping frames from several rail videos. Then the road portions within the region of interest (ROI) are labelled using a labelling tool. Several labelling tools are available which can be utilized for this purpose. The details regarding one such labelling tool can be found here [LabelMe].

Semantic Segmentation

Semantic segmentation is the task of classifying pixels in the image to a predefined class. Semantic Segmentation has many applications, such as detecting road signs, detecting drivable areas in autonomous vehicles etc. An overview of keras semantic segmentation can be found at these sources – Semantic Segmentation and A Beginner’s guide to Deep Learning based Semantic Segmentation using Keras.

Semantic segmentation network follows an encoder decoder architecture. So the initial step is to choose an appropriate encoder and decoder network as per our problem definition. A standard model such as ResNet, VGG or MobileNet is chosen for the base network usually. When the model is trained for the task of semantic segmentation, the encoder outputs a tensor containing information about the objects, and its shape and size. The decoder takes this information and produces the segmentation maps. A detailed explanation regarding various model architectures for semantic segmentation networks can be found here – A Simple Guide to Semantic Segmentation. 

Once the base network is chosen, we need to choose appropriate segmentation architecture. Several segmentation architectures are available for semantic segmentation such as FCN, SegNet, UNet and PSPNet.

SegNet:

SegNet follows an encoder decoder architecture. The encoder has 13 convolutional layers from VGG-16. While doing 2×2 max pooling, the corresponding max pooling indices are stored. And the decoder uses this to  perform non-linear upsampling.

Image Source

FCN (Fully Convolutional Network) :

The FCN network is an extension of CNN. In normal CNN, we cannot take arbitrary-sized images. This limitation is overcome in FCN; FCNs only have convolutional and pooling layers which give them the ability to make predictions on arbitrary-sized inputs.The three variants are FCN8, FCN16 and FCN32. In FCN8 and FCN16, skip connections are used. More information regarding FCN network can be found here – Fully Convolutional Network.

Image Source

Implementation

We use Keras library for training and testing the semantic segmentation network. Keras segmentation has all the utilities required for that.

Generating ROI

We are focussing the detections within a region of interest. An ROI can be drawn by mouse interaction and opencv functionalities and then applying a four point transform on the image. Label the concrete portions in the road rail intersection areas within the ROI using LabelMe as mentioned earlier. LabelMe generates json files corresponding to annotations.

Training and Testing

Using the annotated jsons from LabelMe, corresponding mask images are generated and these mask images are used for further training. Several pretrained encoder-decoder networks like resnet50_segnet, mobilenet_unet, resnet50_unet etc. are available, which we can choose based on the requirement. Our custom base model will efficiently generate feature vectors from these mask images and these features are given to our custom decoder network for result vector generation. While testing using our custom model, the output will be a mask corresponding to road portions and these predicted mask images can be mapped to our original image using opencv functionalities. 

Accuracy

Our custom semantic segmentation network efficiently detects the road portions in the railroad intersections with an accuracy of 99%.

Conclusion

The system described will efficiently detect the road portions in the railroad intersections areas. Our obtained results are very promising in terms of detection rate and also have less inference time. As rail networks expand across the globe, the number of intersections, junctions, and level crossings increase, resulting in potential safety hazards. The AI-based module presented in this blog can help improve rail crossing safety.

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      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.

       

      RAJIN RAVIMONY Distinguished Engineer

      RAJIN RAVIMONY

      Distinguished Engineer

       

      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.

       

      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.

       

      Rajin holds an MS in Micro-electronics from BITS Pilani.

       

      SIBY ABRAHAM Executive Vice President, Strategy

      SIBY ABRAHAM

      Executive Vice President, Strategy

       

      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.

       

      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.

       

      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.

       

      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut

       

      SUDIP NANDY

      Board Member

       

      An accomplished leader with over 40 years of experience, Sudip has helped build and grow companies in India, the US and the UK.

      He has held the post of Independent Director and Board Member for several organizations like Redington Limited, Excelra, Artison Agrotech, GeBBS Healthcare Solutions, Liquid Hub Inc. and ResultsCX.

      Most recently, Sudip was a Senior Advisor at ChrysCapital, a private equity firm where he has also been the Managing Director and Operating Partner for IT for the past 5 years. During his tenure, he has been Executive Chairman of California-headquartered Infogain Corporation and the non-Exec Chair on the board of a pioneering electric-2-wheeler company Ampere Vehicles, which is now a brand of Greaves Cotton Ltd.

      Earlier on in his career, Sudip has been the CEO and then Chairman India for Aricent. Prior to that, he had spent 25+ years in Wipro where he has been the Head of US business, Engineering R&D Services, and later the Head of EU Operations.

      Sudip is an active investor in several interesting startups in India and overseas, which mostly use technology for the social good, encompassing hyperlocal, healthcare, rural development, farmer support and e2W ecosystem. He also spends time as a coach and mentor for several CEOs in this role.

       

      SUJEETH JOSEPH Chief Product Officer

      SUJEETH JOSEPH

      Chief Technology Officer

       

      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.

       

      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.

       

      He graduated in Electronics Engineering from Bombay University in 1991.

       

      SUJITH MATHEW IYPE Co-founder & CTO

      SUJITH MATHEW IYPE

      Co-founder & COO

       

      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.

       

      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".

       

      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.

       

      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.

       

      RAMESH SHANMUGHAM Co-founder & COO

      RAMESH SHANMUGHAM

      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.