In this article, we are going to see how we can easily integrate an Object Detection engine in your project and how to choose and access the right engine according to your data.
In the beginning of the 2000s, the first object detection engines were handmaded due to the lack of effective image representation at that time.
Originally proposed in 2005 by N. Dalal and B. Triggs, the Hog Detector is an improvement of the scale invariant feature transform and shape contexts of its time.HOG works with something called blocks, a dense pixel grid in which gradients are constituted from the magnitude and direction of change in the intensities of pixels within the block. HOGs are widely known for their use in pedestrian detection. To detect objects of different sizes, the HOG detector rescales the input image for multiple times while keeping the size of a detection window unchanged.
Between 2005 and 2015, multiple object detection evolutions were created: Deformable Part-based Model (DPM) then deep learning approaches (AlexNet, RCNN? SSPnet, FastRCNN, FPN, etc.).
Object detection is a computer vision technique that works to identify and locate objects within an image or video. Specifically, object detection draws bounding boxes around these detected objects, which allow you to locate where objects are in a given scene. Object detection is different from image recognition which labels an entire image.
You can use Object Detection in numerous fields, here are some examples of common use cases:
When you need a Object Detection engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
There are numerous Object Detection engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Object Detection technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Object Detection. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Object Detection engines with a provider as a simple parameter. With only a few lines, you can set up your project in production.
You are a solution provider and want to integrate Eden AI, contact us at : firstname.lastname@example.org