Manufacturing Excellence With Image Recognition Models
On average, the cost of poor product quality for manufacturing industries is about 20% of the total sales. Quality control plays a crucial role in many industries, and the ability to detect and identify surface defects is of utmost importance. Traditional manual inspection methods rely on human perception and judgment and often fall short in time consumption, subjectivity, and human error.
However, with advancements in artificial intelligence and image recognition models, it is now possible to automate surface defect detection processes with greater accuracy and efficiency. This blog will explore leveraging image recognition models for surface defect detection and discuss an example use case in the steel industry. By breaking down the inspection process into distinct steps, we aim to understand how AI-powered systems can accurately detect and classify surface defects.
Challenges in Surface Defect Detection
Various complications in surface defect detection for industries including manufacturing, automotive, electronics, and textile can lead to flaws in product quality. The complexity of manufacturing faults poses a significant barrier for organizations, potentially leading to compromised product integrity and customer dissatisfaction. The breakneck speeds at which production lines operate demand rapid defect identification mechanisms, emphasizing the urgency for real-time detection solutions. Some of the key obstacles to effective defect detection are:
● Defect diversity and complexity: Manufacturing processes can result in various defects, varying in size and complexity. For instance, in automotive manufacturing, defects might range from subtle paint imperfections to structural abnormalities, making consistent detection and classification a demanding task.
● High production speeds: Industries like consumer electronics require rapid defect identification to prevent flawed items from reaching the market. For instance, quickly identifying soldering issues in PCB assembly is crucial to maintaining product reliability and customer satisfaction.
● Real-time processing: The pharmaceutical industry needs real-time detection to ensure product safety and compliance. For instance, detecting defects in pill coating prevents compromised medication quality and potential regulatory issues.
● Manual visual inspection: Involves scrutinizing products for surface defects and irregularities. Due to the manual process, it can be time-consuming, especially for large quantities, leading to workflow delays. It is also prone to defect oversight or misclassification during prolonged inspection periods. Manual inspection heavily relies on individual expertise, which may lack scalability and availability.
Benefits of using AI
AI-based visual inspection offers a promising solution to overcome the challenges faced during manual visual inspection in the manufacturing industry.
● By leveraging artificial intelligence and image recognition models, AI-based systems can provide consistent and objective defect detection, minimizing the impact of human subjectivity.
● These systems can analyze large volumes of data with remarkable speed and accuracy, significantly reducing inspection time and improving overall efficiency.
● AI models can be trained to detect even subtle or hard-to-identify defects that may go unnoticed by human inspectors, surpassing the limitations of human visual perception and enhancing the overall accuracy of defect identification.
● Unlike manual inspections that heavily rely on the skill and expertise of individual inspectors, AI-based visual inspection is not dependent on individual proficiency, making it scalable and adaptable across different inspection scenarios.
● With continuous learning and improvement, these systems can evolve to handle complex defect patterns and provide increasingly reliable and efficient quality control.
Three stages of defect handling
Image detection models integrate the power of deep learning and a meticulously designed framework to accomplish multiple tasks with great accuracy. It excels in the key stages of defect handling: detection, classification, and localization providing a superior solution to conventional methods.
By employing these three stages of defect handling, industries can streamline their quality control processes and ensure effective remedial measures are taken promptly.
Next-generation AI-driven visual inspection
At Sigmoid, we have developed a solution that harnesses cutting-edge deep-learning algorithms specifically crafted for image processing. A crucial component is its meticulous optimization of each stage within the defect-handling process, utilizing tailored architectures that focus on specific aspects to ensure exceptional performance.
Detection and classification: The first two stages, detection and classification, use a pre-trained CNN architecture designed to improve the efficiency and effectiveness of feature extraction. This pre-trained model has already undergone extensive training on a large dataset. It is especially beneficial when we have limited data specific to the use case. Various augmentation techniques are employed to ensure our framework's robustness and reliability further, increasing its effectiveness in real-world scenarios.
Localization: This stage utilizes a dedicated deep learning architecture designed explicitly for semantic segmentation, where the goal is to classify each pixel and delineate object boundaries. It consists of an encoder pathway to capture contextual information and a symmetric decoder pathway to recover spatial details. This structure aids in capturing both global and local features crucial for accurate localization. Moreover, each distinct defect type possesses its individualized localization model, adept at encapsulating distinctive features inherent to that defect.
Throughout this process, our solution maintains a high accuracy rate across all three stages of defect handling. An illustration of our proprietary solution framework is given below:
Leveraging image recognition models for surface defect detection heralds a new era in quality control. AI-powered systems offer consistent, objective detection, speeding up the process and improving accuracy. They identify subtle defects, surpassing human capabilities, and are scalable across various scenarios. Embracing this technology reduces costs, enhances product reliability, and boosts competitiveness, marking a significant step forward in manufacturing efficiency and excellence.
Sigmoid’s principal data scientist Debapriya Das and associate lead data scientist Subodh Rai wrote this article.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/May Lim; Diagrams: Sigmoid
