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Object Detection in AI Artificial Intelligence
Object Detection in AI Artificial Intelligence
As an important category of artificial intelligence, computer vision is a computer and software system science that can identify and understand images and scenes. This category mainly includes image recognition, purpose detection, image generation, image super-resolution and other directions. Because there are many practical cases in the ideal, purpose detection should be the most in-depth direction in computer vision. In this tutorial, we will briefly introduce concepts including the detection of the current purpose, challenges faced by software developers, corresponding processing plans, and coding tutorials for performing high-performance inspections.
Purpose detection refers to the computer and software system's ability to locate and identify each goal in the image/scene. It has been commonly used in face detection, vehicle detection, pedestrian counting, network images, safe systems, and driverless cars. There are currently many methods of detection that can be applied in theory. Like any other computer technology, all kinds of surprising and purposeful detection methods are from computer programmers and software developers.
Using purpose detection methods in applications and systems and building new applications based on these methods is not a direct task. In the early days, the completion of the purpose detection included the use of some classic algorithms, such as those supported in the favored computer vision library OpenCV. However, these classic algorithms cannot reach the same excellent performance under different conditions.
After 2012, the disruption of deep learning technology and its rapid application brought many high-precision detection methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet, etc., as well as SSD and YOLO as the representatives. And quasi purpose detection algorithm. To use these deep-learning-based purpose detection methods, we need an in-depth understanding of mathematical learning and deep learning frameworks. Millions of professional computer programmers and software developers want to integrate and create new products for purpose detection. However, knowing and applying the purpose to detect product requirements in an extra and complicated way, this technology is beyond the capabilities of ordinary programmers.
A few months ago, my team recognized this issue, which is why I and John Olafenwa built ImageAI. This is a Python based library that allows programmers and software developers to easily integrate state-of-the-art computer vision into their existing or new applications.
To use ImageAI to accomplish purpose detection tasks, what you need to do is:
1. Install Python on a computer system
2. Install ImageAI and its dependent libraries
3. The model file detected under the target
4. Run sample code
Purpose detection refers to the computer and software system's ability to locate and identify each goal in the image/scene. It has been commonly used in face detection, vehicle detection, pedestrian counting, network images, safe systems, and driverless cars. There are currently many methods of detection that can be applied in theory. Like any other computer technology, all kinds of surprising and purposeful detection methods are from computer programmers and software developers.
Using purpose detection methods in applications and systems and building new applications based on these methods is not a direct task. In the early days, the completion of the purpose detection included the use of some classic algorithms, such as those supported in the favored computer vision library OpenCV. However, these classic algorithms cannot reach the same excellent performance under different conditions.
After 2012, the disruption of deep learning technology and its rapid application brought many high-precision detection methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet, etc., as well as SSD and YOLO as the representatives. And quasi purpose detection algorithm. To use these deep-learning-based purpose detection methods, we need an in-depth understanding of mathematical learning and deep learning frameworks. Millions of professional computer programmers and software developers want to integrate and create new products for purpose detection. However, knowing and applying the purpose to detect product requirements in an extra and complicated way, this technology is beyond the capabilities of ordinary programmers.
A few months ago, my team recognized this issue, which is why I and John Olafenwa built ImageAI. This is a Python based library that allows programmers and software developers to easily integrate state-of-the-art computer vision into their existing or new applications.
To use ImageAI to accomplish purpose detection tasks, what you need to do is:
1. Install Python on a computer system
2. Install ImageAI and its dependent libraries
3. The model file detected under the target
4. Run sample code