How Object Detection Works

One-Stage and Two-Stage

The model described above works well as long as there is one object in the image. But a practical algorithm should be able to detect unlimited objects in any image. Therefore, our algorithm must be able to detect all objects and draw multiple boxes with different dimensions (also known as windows) around them. There are two general methods to determine the right window size. Both of these methods use different classes of object detection algorithms.

The first type of these algorithms first uses classical CV methods to scan the image and select regions with a high probability of an object present. Then an object detection algorithm consisting of several regression and classification models labels the object inside selected regions. These types of algorithms are categorized under the “two-stage” method; they are often more accurate but slower than one-stage methods.

The second  type of object detection algorithms examines and detects objects only in specific places and sizes. These locations and sizes are strategically chosen to cover as many scenarios as possible. The algorithms in this group usually divide an image into several sections of a specific size and assume a certain number of objects in each area, each of which has a predetermined shape and size. Algorithms in this category are called “one-stage” methods, examples include YOLO, SSD, and RetinaNet. In general, one-=stage algorithms are faster but less accurate.

The AIEX platform provides a variety of object detection algorithms in several frameworks, which can easily be used without any coding knowledge.

Table1: The AIEX object detection algorithms 

Let’s take a closer look at one important algorithm from each group.