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Man rolling a tyre
  • Jeevan and Neel
  • March 17, 2021

Deep Learning based Computer Vision System for Automated Tyre Defect Detection

Business photo created by freepik.com

As the global manufacturing industry faces the pressures of bringing to market multiple variants of the highest quality products in the shortest possible time, a shift towards AI-driven automation across all functions has become inevitable.

In quality inspection, AI-driven computer vision systems are already enabling the streamlining of the production process so that the products are compliant with the quality criteria set by the company. This in turn brings in advantages of greater efficiency, lower operational costs while enabling 24/7 production and quicker decision making.  

Global Tyre manufacturers have been one of the early adopters of AI-powered technology in various areas including quality assurance. One major application of AI here is defect detection in tyres using deep learning-based computer vision systems. Due to the nature of raw materials used in the tyre manufacturing process, tyre components may be contaminated by metallic or nonmetallic impurities (e.g., steel threads, screws, and plastic fragments), bubble, and overlap. When a vehicle with a defective tyre is run at high speeds, these defects lead to reduced lifetime or even blowout of the tyre.

Background 

The Indian tyre industry manufactures 150+ distinct types of tyres with diameters varying from 8” to 19” and width from 400mm to 1000mm for applications ranging from bikes to cranes. To add to this, they are required to detect 80+ classes of tyre defects and are constantly struggling with a dearth of trained human resources. 

Their challenges with human resources are manifold – shortage of experienced manpower, time taken in nurturing new resources to the expertise level required, person dependency in identifying the faults and repeatability as teams change. To overcome these challenges, tyre manufacturers need an automated solution for tyre quality inspection and defect detection.  

In one of our customer engagements, Ignitarium engineers discovered that the major problems to be solved, to accurately detect defects in tyres were:

  • Mimicking the manual test and integrating to the existing manufacturing and quality control process flow
  • Having a mechanical fixture which will auto adjust to the varied tyre sizes placed by an operator and also rotate so that internal and external walls of the tyre are captured in one rotation.
  • Capturing images at an optimum speed of rotation, processing, analysing and detecting the faults. 
  • Stopping at each faulty location and marking the defects on the tyre
  • Easy and safe release of the tyre

Type of defects in tyres

 Out of the 80+ classes of possible defects commonly occurring during tyre manufacturing, segregation is done to create severity sets. Various tyre manufacturers have their own specific categorisation methods based on their unique chemical recipes and mechanical manufacturing processes. Usually around 30+ of these classes are considered to be of high severity. A limited sampling of these defect classes are depicted in the images below:

Fig 1: Tyre with Defects: Left to Right: SWC – Side Wall Crack, RLC – Rim Line Crack, SWB – Side Wall Blister, Stencil number offset, Chamfer defect

Tyre Defect Detection

  A typical method of tyre quality inspection is to use X-ray machines and to use techniques like Weighted Texture Dissimilarity. However, X-ray machines require a lot of space and are very expensive. Instead, we used a RGB camera to capture images of the tyre. To solve tyre defect detection problems, instance segmentation (combination of object detection and segmentation) was chosen, so that each instance of each defect featured in the image could be identified, instead of categorizing each pixel like in semantic segmentation.

Data Generation

For data generation, images captured from multiple tyres with already identified and marked defects were used. A 1080p resolution camera was used to capture videos of faulty and ‘good’ images. Image capture was done from various angles to improve generalisation.

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 and making corrections to data. The data was collected by dumping frames from several generated videos. Then the defect portions within the region of interest (ROI) were labelled using labelling tools like LabelMe.

Instance Segmentation

Instance segmentation is a technique in which an object is detected and then masked at an individual pixel level. It combines 1) Object detection, in which each individual object in a frame is classified and localized and 2. Semantic segmentation in which each pixel is segregated into pre-defined classes. Instance segmentation allows a label to be attached to each pixel of an image.

Mask-RCNN

Mask-RCNN is a deep neural network that addresses instance segmentation problems in computer vision. Mask R-CNN is a combination of two well-known network topologies –  Faster R-CNN and a Fully Connected Network (FCN). Mask-RCNN follows a 2-step process. In the first step, for each input image, proposals are generated about the regions where an object might exist. In the second step, the class of the object is predicted and a pixel level mask is generated for the object based on the proposal generated from the first step. 

[Source Link]

Implementation

We build our inference pipeline using a customized variant of Mask-RCNN. The base Keras implementation can be found at https://github.com/matterport/Mask_RCNN.git and the corresponding reference paper can be found at https://arxiv.org/pdf/1703.06870.pdf 

Inference Time

Inference time of the un-optimized model is listed in the following table. 

Architecture Environment CPUTimings GPU Timings(Quadro P4000)
Mask-RCNN Keras-TF
(640*480)		 5 sec/image 3sec/image

With model optimization techniques, the per image latency can be reduced to less than 150ms.

Accuracy

Our customized model was able to achieve a detection accuracy of 96%.

Conclusion

This R&D effort to detect defects in tyres indicates that it is possible to achieve significant automation of the tyre quality inspection process. In a practical system, where high inspection throughput is mandatory, an automated inspection jig would be called for. This would include multiple cameras to simultaneously view the outer and inner surfaces of the tyre, a motor assembly to rotate the tyre 360 degrees and a control system that can synchronize the tyre movement, image capture and detection. 

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      SUJEET SREENIVASAN

      Senior Vice President – Automotive Technology

       

      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.