Here is our selection of the best Object Detection APIs to help you choose and access the right engine according to your data.
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 involves labeling an entire image.
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In the early 2000s, the first object detection engines were developed manually due to the lack of efficient 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.).
API4AI is a solution that uses object detection technology to analyze images and detect various objects within them. The algorithm can detect multiple objects in a single image and provide coordinates to draw bounding boxes around each object. Additionally, it can classify each object and provide the most likely class along with a confidence level for the classification.
Amazon Rekognition Image can return the bounding box for common object labels such as cars, furniture, apparel or pets. Bounding box information isn't returned for less common object labels. It provides bounding boxes to find the exact locations of objects in an image, count instances of detected objects, or to measure an object's size using bounding box dimensions.
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Chooch AI is a visual detection platform that quickly replicates human visual tasks and processes with AI on the edge & in the cloud. With high accuracy and fast response time, Chooch AI is the leader both in computer vision training and deployment for true Visual AI on the edge and in the cloud. Chooch provides complete artificial intelligence solutions in healthcare, geospatial, media, security, retail and industrial applications.
Clarifai is a leading provider of AI-powered visual recognition solutions, offering state-of-the-art object detection capabilities through its API. Their object detection API uses deep learning models to identify and locate objects within images and videos, providing highly accurate results in real-time. One of the key benefits of Clarifai's API is its ability to detect and recognize a wide range of objects, including people, animals, and various objects, making it highly versatile for different use cases. Additionally, Clarifai's API is known for its ease of use, with simple integration and flexible pricing options.
The Vision API can detect and extract multiple objects in an image with Object Localization.
Object localization identifies multiple objects in an image and provides a LocalizedObjectAnnotation for each object in the image. Each LocalizedObjectAnnotation identifies information about the object, the position of the object, and rectangular bounds for the region of the image that contains the object.
Visua AI has built a visual classification tool that focuses on extracting the most relevant signals from media. Specifically built for the needs of platforms and specialist providers, the technology makes it easier for you to derive meaningful insights for clients.
Imagga is a computer vision artificial intelligence company. Imagga Image Recognition API features auto-tagging, auto-categorization, face recognition, visual search, content moderation, auto-cropping, color extraction, custom training and ready-to-use models. Available in the Cloud and on On-Premise. It is currently deployed in leading digital asset management solutions and personal cloud platforms and consumer facing apps.
Microsoft Azure offers a variety of services for object detection, such as Azure Cognitive Services with Computer Vision API, Azure Machine Learning, Azure IoT Edge, Azure Kubernetes Service (AKS) and Custom Vision AI. These services allow users to use pre-trained models, train and deploy custom models, run object detection on IoT devices, scale and manage models in a Kubernetes cluster, and easily train, deploy, and improve custom image classifiers with object detection support.
SentiSight.ai uses deep learning algorithms to analyze images and detect objects within them. The software can detect multiple objects in an image, including objects of different sizes and orientations, and provide a bounding box around each object. SentiSight.ai is able to classify the objects it detects and can also track objects across multiple frames in a video stream and analyze the movement of objects in a scene.
Hive Object Detection API provides pre-trained models for object detection in various domains, such as computer vision and autonomous vehicles, which can be used to identify and locate objects in images and videos. tonomous vehicles, which can be used to identify and locate objects in images and videos. The API also allows developers to train custom object detection models on their own data, and then deploy the models to perform inference. Their deep learning models accurately classify subject matter in visual media with simple mapping to IAB's universal content taxonomy.
You can use Object Detection in numerous fields. Here are some examples of common use cases:
These are just a few examples, object detection technology can be applied in many other fields as well, where it can be used to analyze images and videos to extract valuable information and automate numerous tasks.
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Object Detection tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment analysis API, Summarization, Question Answering, Data Anonymization, Speech recognition, and so forth.
We want our users to have access to multiple Object Detection engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs:
You need to set up a provider API that is requested if and only if the main Object Detection API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
After the testing phase, you will be able to build a mapping of providers performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best Object Detection API.
You can choose the cheapest Object Detection provider that performs well for your data.
This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because Object Detection APIs will validate and invalidate each other for each piece of data.
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
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You can see Eden AI documentation here.
The Eden AI team can help you with your Object Detection integration project. This can be done by :
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