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Medical devices must meet the highest quality standards to meet patient safety concerns. This means devices must be effective, defect-free, and minimize patient harm. For...
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Introduction Object detection has become a cornerstone in computer vision, enabling machines to not only identify what’s in an image but also where it is. From autonomous driving to manufacturing quality control, object detection is transforming industries. However, training robust object detection models can be a complex and time-consuming task.
With long-standing production processes in place, and lofty daily throughput requirements, finding the right time to rework and innovate a manufacturing workflow can be a challenge. However, this means that manufacturers often hold on to legacy processes for far too long — processes that actually throttle production capacity and generate
With the recent major advancements in AI and machine vision technologies, the appeal of using high-accuracy automated inspection systems to improve manufacturing quality control is quite clear. But, are the up-front implementation costs worth it? When evaluating whether or not AI inspection technology is right for your production process, it’s
Manufacturers of medical devices and critical components consistently struggle with quality control. An over-reliance on manual inspection — and the inherent human error that comes along with it — regularly leads to both financial and environmental waste, as well as potential harm to consumers and to the manufacturer’s brand reputation.
Introduction Image classification is a fundamental task in computer vision with applications in medical imaging, autonomous driving, security, and e-commerce. As deep learning models advance, evaluating them correctly ensures reliability, minimizes errors, and enhances real-world performance. Many data scientists rely on accuracy as the primary evaluation metric. However, accuracy can
The digital age thrives on visual content, from social media graphics to blockbuster movie visuals. Visual storytelling has become indispensable for brands, artists, and creators aiming to captivate their audiences. Enter artificial intelligence (AI), a game-changer in visual content creation. AI tools are enabling creators to produce high-quality visuals faster
In an era of rapidly advancing technology, understanding the nuances between different methodologies is crucial for professionals and enthusiasts alike. Two terms that are often used interchangeably but are distinct in their applications are computer vision and image processing. At Akridata, where we leverage deep learning for advanced image inspections
In recent years, the integration of machine vision systems in robotics has revolutionized industrial processes, automating tasks that once required human intervention. From manufacturing to asset monitoring, these systems have expanded the possibilities for robots, making them more efficient, precise, and capable. In this article, we’ll explore the fundamentals of
Deep learning, a subset of machine learning, has revolutionized industries ranging from healthcare to manufacturing. At the heart of this transformation lies optimizers – key components that fine-tune deep learning models for superior performance. In this guide, we’ll explore what optimizers are, their significance, types, and how they influence the
Classification and clustering are two fundamental concepts in machine learning and data analysis. While both aim to categorize data, their methodologies and applications are distinct. This guide explores the key differences, real-world examples, and use cases of classification and clustering to help you choose the right technique for your project.
Medical imaging has long been a cornerstone of diagnostic medicine, enabling clinicians to visualize the internal structures of the body with remarkable detail. With the advent of deep learning, information processing in medical imaging has reached new heights, transforming accuracy, efficiency, and diagnostic capabilities. This article explores how deep learning
Medical imaging is at the forefront of modern diagnostics, and deep learning has significantly enhanced its capabilities. Among recent advancements, the Modified Double U-Net architecture stands out for its exceptional performance in medical image segmentation. By stacking two U-Net architectures and introducing innovative features, it offers unparalleled accuracy and efficiency
In digital image processing, converting real-world images into a digital format is a crucial first step. Two essential processes, image sampling and quantization, allow computers to transform continuous, analog images into digital form that can be stored, processed, and analyzed. Together, these concepts are fundamental for creating digital images with
In deep learning, having a large, diverse dataset is crucial for training high-performing models. However, gathering extensive data can be costly and time-consuming. This is where data augmentation in deep learning comes in, offering a cost-effective way to expand and diversify training datasets. By applying a variety of transformations to
In today’s data-driven world, businesses rely on data science to transform raw data into valuable insights. However, the process of cleaning, analyzing, and extracting insights from data is complex. This is where a structured pipeline for data scientists becomes crucial, offering a framework for handling data through various stages, ensuring
In today’s AI-driven world, pattern recognition and image analysis are at the heart of transformative applications across various industries. These technologies are crucial for enabling machines to understand and interpret visual information as effectively as humans. For companies like Akridata, which optimize image inspection in sectors such as product manufacturing
Image segmentation in image processing is a fundamental technique that partitions an image into distinct regions to simplify analysis. By breaking an image into meaningful segments, this process enables more effective object detection, classification, and interpretation, paving the way for numerous applications across various industries. This guide provides a detailed
Image segmentation in image processing is a fundamental technique that partitions an image into distinct regions to simplify analysis. By breaking an image into meaningful segments, this process enables more effective object detection, classification, and interpretation, paving the way for numerous applications across various industries. This guide provides a detailed
Morphological image processing is an essential field within computer vision, focusing on the shape and structure of objects within an image. By applying these techniques, images can be refined, enhanced, and analyzed for better object recognition and measurement. This article explores the core techniques and real-world applications of morphological image
In the digital age, the way we interact with visual content is rapidly evolving. Image captioning, a powerful application of artificial intelligence (AI), is at the forefront of this transformation. By automatically generating descriptive captions for images, AI is revolutionizing user experience across various domains. From enhancing accessibility for individuals
In today’s data-driven world, the ability to interpret and act on large volumes of information is a competitive advantage. Data visualization has emerged as a powerful tool that enables businesses to transform raw data into clear, actionable insights, making complex information accessible and easier to understand. With platforms like Akridata,
Image segmentation is a crucial technique in computer vision, used in a wide variety of applications from medical imaging to autonomous vehicles and facial recognition systems. At its core, image segmentation involves partitioning an image into multiple segments or regions to make further analysis easier. One of the most effective,
In the world of artificial intelligence (AI) and machine learning (ML), creating high-performing models depends heavily on one crucial factor: data quality. Two essential processes for building quality datasets are data curation and data labeling. While both are critical for successful AI projects, they serve distinct purposes and play different
Autonomous vehicles (AVs) are at the forefront of AI innovation, promising a future where cars drive themselves, reducing accidents, and making transportation more accessible. However, developing safe and reliable autonomous vehicles requires addressing one of the biggest challenges in AI: detecting and handling edge cases. In the world of autonomous
