Data image augmentation is a technique used in computer vision and deep learning to increase the amount and diversity of data available for training a model. This paper presents an overview of data image augmentation and provides a tutorial on how to perform data image augmentation in Python using the Keras.preprocessing.image The paper also includes a discussion on the benefits and limitations of data image augmentation and provides tips on how to use it effectively. In recent years, computer vision and deep learning have made significant strides in accurately classifying and detecting objects in images. One of the key factors that contribute to the success of these techniques is the availability of large and diverse datasets for training models.

In this tutorial, you will learn to train a custom image classification network for OAK-D using the TensorFlow framework. Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., tomato, brinjal, and bottle gourd). If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. Furthermore, this tutorial acts as a foundation for the following tutorial, where we learn to deploy this trained image classification model on OAK-D. To learn how to train an image classification network for OAK-D, just keep reading. Before we start data loading, analysis, and training the classification network on the data, we must carefully pick the suitable classification architecture as it would finally be deployed on the OAK. Although OAK can process 4 trillion operations per second, it is still an edge device.

Today, almost half of the world's population does not have access to proper healthcare, with many people driven into poverty because of high health expenses. It is estimated that over $140 billion is required annually to meet the health-related sustainable development goal objectives. Further, significant health technology, digital technology, and artificial intelligence (AI) investments are needed to bridge the health service gap in emerging markets. Many health-related startups and tech innovators have started integrating AI with their products and solutions, showing promise of improved diagnoses, reduced costs, and proper access to remote health services. COVID-19 has also accelerated the pace of transition to digital health applications, including those that integrate AI. Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 (Figure 1).

In today's competitive market, having an efficient and flexible supply chain is a significant asset. Hence, companies are looking into ways to optimize their supply chain to help them make decisions to improve their operational efficiency and customer satisfaction and reduce environmental impacts. According to McKinsey reports (Figure 1), AI will define a new "logistics paradigm" by 2030. It will generate $1.3-$2 trillion per year for the next 20 years as it continues to outperform humans at repetitive but mission-critical tasks. In another similar research, McKinsey has reported that businesses, by using AI, can improve their logistics, inventory, and service costs by 15%, 35%, and 65%, respectively. This series is about CV and DL for Industrial and Big Business Applications.

In today's lesson, you will learn the fundamentals of thermal/mid-far infrared vision. By the end of this lesson, you'll have measured the temperature value of each pixel in a thermal image and a thermal video in a very easy way, only using Python and OpenCV. In addition, you'll be able to get the video stream from a thermal camera and the temperature values in real time if you have one of these amazing cameras on hand. To learn how to measure your first temperature value from each pixel in a thermal image, just keep reading. Before we start measuring the temperature value of each pixel, we need to understand the different basic image formats that thermal cameras/images provide.

Transportation is an essential part of our day-to-day life as it enables the carrying of goods from one place to another, trade, commerce, and communication to establish a civilization. The transportation sector has seen multiple revolutions over the past hundred years. Today we are at the stage where a significant breakthrough in transportation is achieved through Artificial Intelligence (AI). AI is already changing the transportation industry by enabling cars, trains, ships, and airplanes to automate autonomously, making the traffic flow smoother. Besides making our lives easier, it can provide a safer, cleaner, smarter, and more efficient transportation mode for everyone. AI-led autonomous transport could, for instance, help reduce human errors involved in many traffic accidents.

Despite the widespread diffusion of renewable energy, oil and gas are among the highly valued commodities in the energy sector. However, commodity cycles, capital planning challenges, and increasing operational risk have propelled the oil and gas industry to make more intelligent and efficient decisions. In a 2018 Ernst & Young (EY) survey, Artificial Intelligence (AI)/Machine Learning (ML) didn't even rank in the top five technologies used by seven global oil and gas supermajors (Figure 1). Further, they feel that in the coming years, technologies like robotic process automation (RPA) (25%) and advanced analytics (25%), but not AI/ML, will have the most significant and positive effect on their businesses. AI/ML have enormous potential in the oil and gas industry, and by not considering it, leaders in the sector risk being blindsided. It can help reduce costs, add capacity and capability, speed decision-making, and improve quality while managing risk.

The agriculture sector is the foundation of any economy. However, with an increase in population, the agriculture sector will feel pressure and need to scale its supplies several times to cope with the increasing consumption. In addition, uncertain factors like climate change, diseases, and infertile land have propelled the sector to adopt innovative approaches like artificial intelligence to protect and increase crop yield. AI has the potential to change the agriculture sector by helping farmers minimize the risk of diseases, proactively adapt to changing climate conditions, monitor the security of crops using drones, etc., while keeping labor costs down (Figure 1). As a result, the overall AI in the agriculture market is projected to grow from an estimated $1B in 2020 to $4B by 2026, at a compound annual growth rate (CAGR) of 25.5% between 2020 and 2026. This series is about CV and DL for Industrial and Big Business Applications.

Today, we will learn about Multi-Task Learning and HydraNets. This is a Deep Learning technique I first introduced back in mid-2020 in an email I sent to exactly 653 people. The responses to this email were so high (engineers from everywhere around the planet told me they loved it and wanted to apply it to their company) I had to create an entire HydraNet section in my course catalog. You can learn more by visiting https://www.thinkautonomous.ai/ Not only is this technique new and exciting for the Deep Learning field, but it's also accessible to many Computer Vision Engineers.

Charles River Analytics Inc. is hiring for Full Time Software Engineer – Computer Vision and Deep Learning (Mid-Senior) - Cambridge, MA - a Senior-level AI/ML/Data Science role offering benefits such as Career development, Competitive pay, Health care, Insurance, Medical leave, Parental leave, Salary bonus