DeepSight and Intel Help Tire Manufacturer Speed Up Flaw Detection

Shanghai DeepSight uses the Intel® Distribution of OpenVINO™ toolkit to help a leading tire manufacturer cut waste and costs and increase efficiencies.

When the tiniest defects can damage a manufacturer’s productivity and profitability, everything matters. Shanghai DeepSight and Intel increased defect detection accuracy to more than 99.9 percent and helped cut labor costs by approximately USD 42,000 per production line.1

The Huge Opportunity in Small Defects

Today, the People’s Republic of China boasts some 600 tire manufacturers that together generate approximately 800 million units annually or fully one-third of the total tire output globally.1

Ensuring consistent product quality at this scale is a challenge. Most companies rely on manual inspection, a time-consuming, and unreliable strategy. It takes around three months to train inspectors, with inspections occupying up to 80 percent of their time. Even fully trained, humans make subjective judgments and are typically only able to maintain a 90 to 95 percent accuracy rate.1

One major tire manufacturer decided to pursue a different path, turning to experienced technology partners for a better solution. The results have been dramatic.

DeepSight Focuses AI on Preventing Defects

Looking for an answer to tire defects, the manufacturer went to Shanghai DeepSight. DeepSight uses state-of-the-art technologies in deep learning, computer vision, and image processing to analyze video in real time and extract key information and insights that make the difference for businesses.

DeepSight brings highly accurate, highly stable testing products that effectively help customers solve problems. The company is also firmly rooted in the industrial sector, having undertaken many successful industry collaborations that focus on industrial defect detection.

Rooting Out Tiny Tire Flaws

Drawing on its AI expertise, DeepSight addressed the tire manufacturer’s ongoing defect challenge through computer vision. Different types of defects need different imaging systems in order to obtain the best images. The DeepSight solution is based on deep learning and relies on tremendous computing capability. The imaging system includes:

  • A camera
  • A lens
  • A light source
  • The mechanical structure (different types of defects require different imaging systems)

The solution’s edge equipment is comprised of:

The image data is first collected by the imaging system. It is then transmitted to the edge device for real-time detection and analysis by DeepSight’s software, Deep Inspect. The results are then communicated back to the production line for classification.

The DeepSight testing equipment replaces the original testing equipment. The configured interface can be directly connected with the customer’s production line, enabling the customer to check the accuracy and stability of the testing equipment through sampling.

DeepSight Partners With Intel to Speed Detection

DeepSight’s core team originated at Intel Labs. So to achieve the best detection results, DeepSight turned to Intel, which brought key technologies to the equation as well as critical experience in AI, computer vision, and deep learning. Intel optimized the algorithm using the Intel® Distribution of OpenVINO™ toolkit.

The toolkit provided computer vision and deep learning inference tools optimized for Intel® processors, resulting in significant performance improvements. By accelerating Intel® hardware with the Intel® Distribution of OpenVINO™ toolkit, DeepSight was able to make its detection model algorithm run stably on one regular industrial computer.

The integrated hardware and software accelerated performance and allowed the DeepSight inspection software to detect defects faster. This saved time on optimization and development and enabled quicker deployment.

Converting Small Defects Into Big Benefits

Automating and accelerating early defect detection allowed the tire maker to maximize production while reducing waste and overall costs. The effort also managed to achieve high stability and a low environmental impact.

Now the problems that cause tire defects can be addressed earlier in the manufacturing process instead of after the tires roll off the production line. Today, thanks to the Intel®-based DeepSight solution, the manufacturer is able to inspect more than 20,000 tires per day in real time with an improved accuracy rate of more than 99.9 percent.1

Detection speed has also dramatically improved over traditional manual inspection, now averaging less than one second per inspection, a dramatic improvement in detection time over previous methods. Better speed and accuracy have benefited the bottom line as well, helping drive down labor costs by approximately USD 42,000 per production line.1

The solution runs fast, matching the speed of the original production line, an achievement much appreciated by the tire manufacturer. It also offers strong scalability and expedited learning in the case of new defects. Based on this early success, the DeepSight solution has since been deployed in additional production lines and is slated to be used for other related tire inspections as well.

DeepSight and Intel Help Tire Maker Shift Into High Gear

Working together, DeepSight technology, Intel® Core™ processors, and the Intel® Distribution of OpenVINO™ toolkit enabled DeepSight’s manufacturing customer to quickly identify things its human inspectors had not been able to see before while also eliminating inspection tasks that can strain inspectors’ eyesight. And because machine inspection is objective and free from fatigue, it can run continuously and with greater speed and efficiency.

By empowering the company to address the root cause of tire defects before they become bigger problems, DeepSight helped the tire maker achieve welcome improvements in production efficiency, deployment speed, worker productivity, waste reduction, and product quality.

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通知および免責事項

インテル® テクノロジーの機能と利点はシステム構成によって異なり、対応するハードウェアやソフトウェア、またはサービスの有効化が必要となる場合があります。実際の性能はシステム構成によって異なります。絶対的なセキュリティーを提供できるコンピューター・システムはありません。詳細については、各システムメーカーまたは販売店にお問い合わせいただくか、http://www.intel.co.jp を参照してください。// 性能に関するテストに使用されるソフトウェアとワークロードは、性能がインテル® マイクロプロセッサーだけに最適化されていることがあります。SYSmark* や MobileMark* などの性能テストは、特定のコンピューター・システム、コンポーネント、ソフトウェア、操作、機能を使用して測定したものです。結果はこれらの要因によって異なります。製品の購入を検討される場合は、他の製品と組み合わせた場合の本製品の性能など、ほかの情報や性能テストも参考にして、パフォーマンスを総合的に評価することをお勧めします。詳細については、https://www.intel.co.jp/benchmarks (英語) を参照してください。// 性能の測定結果はシステム構成の詳細に記載された日付時点のテストに基づいています。また、現在公開中のすべてのセキュリティー・アップデートが適用されているとは限りません。詳細については、公開されている構成情報を参照してください。絶対的なセキュリティーを提供できる製品やコンポーネントはありません。// 記載されているコスト削減シナリオは、指定の状況と構成で、特定のインテル® プロセッサー搭載製品が将来のコストに及ぼす影響と実現されるコスト削減の例を示すためのものです。状況によって異なる可能性があります。インテルは、いかなるコストもコスト削減も保証いたしません。// インテルは、本資料で参照しているサードパーティーのベンチマーク・データまたはウェブサイトについて管理や監査を行っていません。本資料で参照しているウェブサイトにアクセスし、本資料で参照しているデータが正確かどうかを確認してください。// いくつかのテスト結果は、インテル社内での分析またはアーキテクチャーのシミュレーションあるいはモデリングで推定 / シュミレートされており、情報提供を目的として提供されています。システム・ハードウェア、ソフトウェア、構成などの違いにより、実際の性能は掲載された性能テストや評価とは異なる場合があります。

免責事項

1Shanghai DeepSight research and testing results.