#object detection

[alphatree]

Aircraft Detection on VHR imagery

Results of aircraft detection (accuracy of 100%) © Atlas AI, on varying resolution VHR imagery (Maxar, Satellogic, Airbus). Detecting and classifying aircrafts allows us to better understand the commercial activity in key locations around the world in support of our socio-economic analyses.

The method features advanced deep learning technologies, and model training uses data acquired from a rich pool of diverse imagery, all normalized to uniform standards using proprietary algorithmics.

[biocube-technologies-inc]

Why Should IITs Implement Face Recognition Technology?

Our latest blog post explores the transformative power of face recognition technology! Discover why IITs should embrace Face Recognition Technology innovation and how it can significantly enhance their educational institutions.

From improving attendance and video surveillance to object detection, activity monitoring, and identification, face recognition technology offers comprehensive benefits for campus safety and efficiency.

[tagx01]

Guide to Image Classification & Object Detection

Computer vision, a driving force behind global AI development, has revolutionized various industries with its expanding range of tasks. From self-driving cars to medical image analysis and virtual reality, its capabilities seem endless. In this article, we'll explore two fundamental tasks in computer vision: image classification and object detection. Although often misunderstood, these tasks serve distinct purposes and are crucial to numerous AI applications.

The Magic of Computer Vision:

Enabling computers to "see" and understand images is a remarkable technological achievement. At the heart of this progress are image classification and object detection, which form the backbone of many AI applications, including gesture recognition and traffic sign detection.

Understanding the Nuances:

As we delve into the differences between image classification and object detection, we'll uncover their crucial roles in training robust models for enhanced machine vision. By grasping the nuances of these tasks, we can unlock the full potential of computer vision and drive innovation in AI development.

Key Factors to Consider:

Humans possess a unique ability to identify objects even in challenging situations, such as low lighting or various poses. In the realm of artificial intelligence, we strive to replicate this human accuracy in recognizing objects within images and videos.

Object detection and image classification are fundamental tasks in computer vision. With the right resources, computers can be effectively trained to excel at both object detection and classification. To better understand the differences between these tasks, let's discuss each one separately.

Image Classification:

Image classification involves identifying and categorizing the entire image based on the dominant object or feature present. For example, when given an image of a cat, an image classification model will categorize it as a "cat." Assigning a single label to an image from predefined categories is a straightforward task.

Key factors to consider in image classification:

Accuracy: Ensuring the model correctly identifies the main object in the image.

Speed: Fast classification is essential for real-time applications.

Dataset Quality: A diverse and high-quality dataset is crucial for training accurate models.

Object Detection:

Object detection, on the other hand, involves identifying and locating multiple objects within an image. This task is more complex as it requires the model to not only recognize various objects but also pinpoint their exact positions within the image using bounding boxes. For instance, in a street scene image, an object detection model can identify cars, pedestrians, traffic signs, and more, along with their respective locations.

Key factors to consider in object detection:

Precision: Accurate localization of multiple objects in an image.

Complexity: Handling various objects with different shapes, sizes, and orientations.

Performance: Balancing detection accuracy with computational efficiency, especially for real-time processing.

Differences Between Image Classification & Object Detection:

While image classification provides a simple and efficient way to categorize images, it is limited to identifying a single object per image. Object detection, however, offers a more comprehensive solution by identifying and localizing multiple objects within the same image, making it ideal for applications like autonomous driving, security surveillance, and medical imaging.

Similarities Between Image Classification & Object Detection:

Certainly! Here's the content presented in a table format highlighting the similarities between image classification and object detection:

By presenting the similarities in a tabular format, it's easier to grasp how both image classification and object detection share common technologies, challenges, and methodologies, despite their different objectives in the field of computer vision.

Practical Guide to Distinguishing Between Image Classification and Object Detection:

Building upon our prior discussion of image classification vs. object detection, let's delve into their practical significance and offer a comprehensive approach to solidify your basic knowledge about these fundamental computer vision techniques.

Image Classification:

Image classification involves assigning a predefined category to a visual data piece. Using a labeled dataset, an ML model is trained to predict the label for new images.

Single Label Classification: Assigns a single class label to data, like categorizing an object as a bird or a plane.

Multi-Label Classification: Assigns two or more class labels to data, useful for identifying multiple attributes within an image, such as tree species, animal types, and terrain in ecological research.

Practical Applications:

Digital asset management

AI content moderation

Product categorization in e-commerce

Object Detection:

Object detection has seen significant advancements, enabling real-time implementations on resource-constrained devices. It locates and identifies multiple objects within an image.

Future Research Focus:

Lightweight detection for edge devices

End-to-end pipelines for efficiency

Small object detection for population counting

3D object detection for autonomous driving

Video detection with improved spatial-temporal correlation

Cross-modality detection for accuracy enhancement

Open-world detection for unknown objects detection

Advanced Scenarios:

Combining classification and object detection models enhances subclassification based on attributes and enables more accurate identification of objects.

Additionally, services for data collection, preprocessing, scaling, monitoring, security, and efficient cloud deployment enhance both image classification and object detection capabilities.

Understanding these nuances helps in choosing the right approach for your computer vision tasks and maximizing the potential of AI solutions.

Summary

In summary, both object detection and image classification play crucial roles in computer vision. Understanding their distinctions and core elements allows us to harness these technologies effectively. At TagX, we excel in providing top-notch services for object detection, enhancing AI solutions to achieve human-like precision in identifying objects in images and videos.

Visit Us, www.tagxdata.com

Original Source, www.tagxdata.com/guide-to-image-classification-and-object-detection

[danieldavidreitberg]

Autonomous Vehicles: LiDAR Modeling and Sharps Object Detection by Daniel Reitberg

Driving cars that drive themselves just got easier. Advanced self-driving car companies are using LiDAR (Light Detection and Ranging) technology more and more to improve how they see and find objects. While LiDAR systems make high-resolution, point-by-point models of their surroundings, they work like very fast 3D laser scanners, sending out millions of pulses per second. However, LiDAR works well in low light, rain, and fog, while cameras have trouble with changing lighting conditions.

Self-driving cars can "see" the world in new and amazing detail thanks to this accurate 3D mapping. LiDAR is a very accurate way to tell the difference between things like people walking, bicycles, and even abandoned cars. This means better navigation, especially in cities with lots of traffic and surprising obstacles. With LiDAR, self-driving cars can see their surroundings clearly and completely, which lets them respond quickly and correctly to changing conditions on the road.

LiDAR is a big step forward in the development of self-driving cars. Self-driving cars can travel more safely and more efficiently in the future thanks to LiDAR's better ability to find objects.

[softlabsgroup05]

Explore the fundamental elements of computer vision in AI with our insightful guide. This simplified overview outlines the key components that enable machines to interpret and understand visual information, powering applications like image recognition and object detection. Perfect for those interested in unlocking the capabilities of artificial intelligence. Stay informed with Softlabs Group for more insightful content on cutting-edge technologies.

[imarticusblog]

Object detection and its Real-World Applications

The real-world applications of object detection can be seen in many crucial areas of our lives such as medical imaging, video tracking, movement detection, facial recognition, object recognition and even in autonomous vehicles.

[image-classification]

Image Classification vs Object Detection

Image classification, object detection, object localization — all of these may be a tangled mess in your mind, and that's completely fine if you are new to these concepts. In reality, they are essential components of computer vision and image annotation, each with its own distinct nuances. Let's untangle the intricacies right away.We've already established that image classification refers to assigning a specific label to the entire image. On the other hand, object localization goes beyond classification and focuses on precisely identifying and localizing the main object or regions of interest in an image. By drawing bounding boxes around these objects, object localization provides detailed spatial information, allowing for more specific analysis.

Object detection on the other hand is the method of locating items within and image assigning labels to them, as opposed to image classification, which assigns a label to the entire picture. As the name implies, object detection recognizes the target items inside an image, labels them, and specifies their position. One of the most prominent tools to perform object detection is the “bounding box” which is used to indicate where a particular object is located on an image and what the label of that object is. Essentially, object detection combines image classification and object localization.

[sitenesia]

Merevolusi Pembuatan Konten: Pengertian, Apa itu Gambar AI? Manfaat, Fitur, serta Bagaimana Dampak, Cara Kerja dan Aplikasi AI Image Resizer atau Pengubah Gambar AI!

Merevolusi Pembuatan Konten, Pengertian dan Apa itu Gambar AI? Manfaat, Fitur, serta Bagaimana Dampak, Cara Kerja dan Aplikasi Pengubah Gambar AI atau AI Image Resizer! Dalam lanskap pembuatan konten digital yang terus berkembang, integrasi kecerdasan buatan atau yang sekarang, khususnya di tahun ini lebih dikenal dengan istilah Artificial Intelligence (AI) telah membawa transformasi yang…

[intozitech]

Visionary Guide to Unattended Object Detection using AI Video Analytics

The rise of artificial intelligence (AI) has revolutionized the way we look at security and surveillance systems. AI video analytics offer an innovative way to detect and monitor objects in a variety of scenarios, a technology that can be used to enhance security, automate processes, and increase accuracy. In this article, we’ll explore the different types of object detection using AI video analytics, and how they can be used to improve safety and security.

Unattended Object Detection

Unattended object detection, also known as abandoned object detection, is a type of object detection that uses AI video analytics to detect objects that have been left behind in an area. This technology can be used to detect objects that are not normally visible to the naked eye, such as suspicious packages or items that have been left behind in public areas. This type of object detection can be used in a variety of scenarios, such as airports, public transportation hubs, and shopping malls, to ensure that no suspicious activity is taking place.

Colour-Based Object Detection

Colour-based object detection is a type of object detection that uses AI video analytics to detect objects based on their colour. This type of object detection can be used to identify objects that are of a certain colour, which can be used to identify individuals, detect objects that need to be removed, or detect objects that are of interest. This type of object detection can be used in a variety of scenarios, such as traffic monitoring, medical imaging, and security systems.

[panged-lin]

I'm going to let my computer science nerd show a little bit, because I wanted to point out a research paper I found personally funny.

YOLOv3: An Incremental Improvement It genuinely reads like a research paper I'd write if I just threw my hands up and gave up being formal. Please give it a read - if not for love of object detection then for love of people who honestly deserve props for publishing it.

Yes, it IS written by the guys who made Darknet if that's any indication. If you saw the Github repository, it absolutely is an indication.

[kaciseonline]

Simplify Detection Challenges with KUS600 Ultrasonic Sensor

Are you facing detection challenges in your industrial or automation processes? The KUS600 Ultrasonic Sensor from Kcsensor might be the solution you've been looking for. Designed to provide accurate and reliable distance and level measurements, the KUS600 sensor offers a range of features that can help you simplify complex detection tasks and improve overall efficiency.

Key Features:

High Accuracy: The KUS600 Ultrasonic Sensor is equipped with advanced signal processing technology, enabling it to achieve high measurement accuracy even in challenging environments. This accuracy is essential for critical applications where precision is paramount.

Long Range Capability: With a range of up to 10 meters (32.8 feet), the KUS600 can cover a wide variety of distance and level measurement needs, making it suitable for both short-range and long-range applications.

Multiple Output Options: The sensor offers various output options, including analog voltage and current outputs, as well as digital outputs. This versatility allows seamless integration with various control systems and programmable logic controllers (PLCs).

Easy Installation: The sensor comes with mounting brackets and hardware, simplifying the installation process. The intuitive interface and adjustable settings further streamline setup, reducing downtime and installation costs.

Non-Contact Measurement: Ultrasonic technology enables non-contact distance measurement, eliminating the need for physical contact with the target object. This feature is particularly useful when dealing with delicate, abrasive, or hazardous materials.

Built-in Temperature Compensation: Temperature fluctuations can affect measurement accuracy. The KUS600 includes built-in temperature compensation, ensuring that measurements remain reliable even when operating in varying temperature conditions.

User-Configurable Parameters: The sensor allows users to adjust various parameters such as measurement range, sensitivity, and output scaling. This flexibility enables customization to suit specific application requirements.

[mustafabukulmez-blog]

Bilgisayar Görüsü Nedir?

Merhaba. Bu yazımızda Bilgisayar Görüsü Nedir?  sorunun cevabına bakacağız. Bu konu elbette yeni bir konu değil ancak başlattığım Üretim ve Yönetim Sistemlerinin Tarihsel Gelişimi serisi için devam niteliğinde olan bir konu olduğu için yazmak istedim. Aynı zamanda videosu da gelecektir. Üretim ve Yönetim Sistemlerinin Tarihsel Gelişimi youtube podcast seri için buraya yazı serim…

[lucabyte]

On autonomy, and what it means to be Obliged to Help.

Bonus:

[danieldavidreitberg]

Object Detection in Autonomous Vehicles

Artificial intelligence and object detection algorithms have taken the wheel in the autonomous vehicle industry. Daniel Reitberg explores how this technology is turning self-driving cars into a safe and reliable reality.

The Road to Autonomous Driving

The dream of self-driving cars is rapidly becoming a reality, thanks to significant advancements in computer vision and artificial intelligence. A crucial element of autonomous driving is object detection, which enables a vehicle to perceive and respond to its surroundings.

How Object Detection Works

Object detection is the process of identifying and classifying objects in an image or video. It relies on a combination of machine learning, deep neural networks, and computer vision. By dividing an image into grids and analyzing each grid for objects, the system can recognize vehicles, pedestrians, road signs, and more.

The Role of Lidar and Radar

Object detection doesn't rely solely on cameras. Lidar (Light Detection and Ranging) and radar are equally essential. Lidar uses lasers to measure distances and create a 3D map of the vehicle's surroundings. Radar employs radio waves to detect objects and their speeds. The fusion of data from these sensors provides a comprehensive view of the environment.

Challenges and Solutions

Object detection isn't without its challenges. Adverse weather conditions, low light, and the unpredictable behavior of pedestrians and other vehicles can make detection difficult. To overcome these challenges, engineers are developing systems that combine multiple sensors, allowing vehicles to navigate safely in various conditions.

Real-Time Decision Making

Once an object is detected, the autonomous vehicle's onboard computer must make split-second decisions. For example, if a pedestrian suddenly steps onto the road, the system must recognize the person, calculate their speed and trajectory, and decide whether to brake or change lanes.

The Human Safety Driver

Despite the remarkable progress in object detection and autonomous driving, human safety drivers are still a critical component of testing and deploying self-driving cars. These drivers can take control if the vehicle encounters a situation it can't handle.

Improving Road Safety

The ultimate goal of object detection in autonomous vehicles is to enhance road safety. The technology has the potential to significantly reduce the number of accidents caused by human error, making our roads safer for everyone.

The Future of Autonomous Driving

The development of object detection and autonomous driving technology is advancing rapidly. In the coming years, we can expect even more sophisticated systems that will bring us closer to the widespread adoption of self-driving cars. As technology continues to evolve, the dream of a future with safer, more efficient roads is well within reach.