The breakthrough and rapid adoption of deep learning in 2012 brought into existence modern and highly accurate object detection algorithms and methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate ones like SSD and YOLO. However, these classical algorithms could not achieve enough performance to work under different conditions. Early implementations of object detection involved the use of classical algorithms, like the ones supported in OpenCV, the popular computer vision library. Getting to use modern object detection methods in applications and systems, as well as building new applications based on these methods is not a straight forward task. Like every other computer technology, a wide range of creative and amazing uses of object detection will definitely come from the efforts of computer programmers and software developers. There are many ways object detection can be used as well in many fields of practice. Object detection has been widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and driverless cars. Object detection refers to the capability of computer and software systems to locate objects in an image/scene and identify each object.
In this tutorial, I will briefly introduce the concept of modern object detection, challenges faced by software developers, the solution my team has provided as well as code tutorials to perform high performance object detection. Object detection is probably the most profound aspect of computer vision due the number practical use cases. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more.
Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. One of the important fields of Artificial Intelligence is Computer Vision.