Automotive

Visual Inspection for Defects Detection

Increase safety and profit by using signal processing and machine vision to prevent defective parts from leaving the assembly line
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Binah.NOW
Automotive Results
99%
Accuracy
300%
Better ROI
X10
Accelerate Time to Market

The Challenge

Reduce production and visual inspection costs and achieve increased operational efficiency with automatic detection and localization

Standard Solution

Human Inspection

Manual inspection produces large numbers of false positives

Some defects too small to be seen

High cost of operations

Significant safety risk

Binah.NOW Visual Inspection

Delivers extreme Accuracy, Stability, and Performance

Eliminates manual review

Finds anomalies quickly using signal processing and machine vision

Accelerates parts delivery while strengthening safety

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The Fourth Industrial Revolution – Industry 4.0

Background

The industry of the future is based on “smart” manufacturing processes that integrate new hardware, software, and communication technologies to increase productivity and reduce costs. One challenge of Industry 4.0 designers is the optimization of the manufacturing processes. A key element is the early – or production-compatible time – detection of defects and production failures. This prevents production errors, increasing productivity and quality and leading to significant economic benefits.

Binah.ai has developed its own algorithmic back-end that solves an endless number of data-driven industrial challenges in real time/quasi-real time with extremely high accuracy.

The flagship use case is using computer vision-based techniques to overcome the limitations of human visual inspection, using images to remotely detect structural damage.

The Challenge of Industrial Anomaly Detection

While most of the available solutions on the market are focused on using classic classifications architecture, many of the challenges are not being met, including:  

  • Defining a representative normal region
  • The boundary between normal and outlying behavior is imprecise
  • The exact notion of an outlier is different for different application domains
  • Lack of availability of labeled data for training/validation
  • Malicious adversaries
  • Data contains noise
  • Normal behavior keeps evolving
  • Defects may have a very small footprint 

Binah.ai's Visual Inspection Advantages

Binah.ai utilizes the combined power of signal processing,  machine learning, and deep learning to create accurate and stable solutions.

Binah.ai’s advantages include:

  • Extremely high accuracy
  • Agnostic – part-type independent
  • Quasi real-time performance  (30/140 milliseconds for in/out of memory)
  • Detecting and alerting about new types of defects
  • Ultra-adaptiveness 
  • Noise/environmental robustness 
  • Availability as a cloud service or on-prem 

When the defects are extremely small and have a very small footprint as compared to the size of the part, an additional architecture layer is used to reveal these defects.

Increase Profits and Reduce Costs with Highly Accurate, Automated Visual Inspection

Eliminate manual post-production visual inspection, reducing production time and investment using Binah.ai’s signal processing-based machine vision anomaly detection

Comparison of Binah.NOW Model Results to Industry Models

Binah.NOW – Plug-and-play

for Automotive AI and Augmented Analytics

Out-of-the-box
Use Cases

No Integration
Necessary

Benefit from
Continuous Validation

Unparalleled Accuracy
and Results

10x Faster Time
to Market

Signal Processing and
Proprietary Back-end Framework

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