Image Annotation Services
ARSRV delivers stellar image annotation services that power AI, machine learning, and data operation strategies.
What is image annotation?
Image annotation is the process of labeling an image, which strategically involves human-powered work and sometimes, computer-assisted help. It is an important step in creating computer vision models for tasks like image segmentation, image classification, and object detection. Image annotation can range from annotating every group of pixels within an image to one label for an entire image.
Successful image annotation projects involving computer vision are built on high-quality annotation. The type of annotation needed will depend on the use case the project is designed for.
What are the types of image annotation services?
ARSRV provides various image annotation services that will cater to a client’s project needs, including bounding boxes, polygon annotations, keypoint annotation, LiDar, semantic segmentation, and image classification. ARSRV’s team works with the client to calibrate the quality and throughput of the job and deliver the best cost-quality ratio as you iterate. We recommend running a sample batch to clarify instructions, edge cases, and approximate task times, before launching full batches.
High-quality image annotation generates ground truth datasets for optimal machine learning functionality. There are numerous types of deep learning applications for image annotation across industries including autonomous technology & transportation, medical AI, commerce, geospatial, finance, government, and more.
Image Annotation Services
Bounding box annotation
It is the most commonly used type of image annotation in computer vision. ARSRV computer vision experts use rectangular box annotation to illustrate objects and train data, enabling algorithms with annotated images to identify and localize objects during the machine learning process. The simplicity of bounding boxes is exactly their strength, making this method of image annotation applicable for a wide range of uses.
Polygon annotation
Expert annotators plot points on each vertex of the target object. Polygon annotation allows all of the object’s exact edges to be annotated, regardless of shape. This allows computer vision and other artificial intelligence models to recognize and respond to objects. This technique is especially useful in computer vision as annotators can use it to identify irregular shapes, allowing computers to identify and respond to them.
Semantic segmentation
Images are segmented into component parts, by the ARSRV team, and then annotated. ARSRV computer vision experts detect desired objects within images at the pixel level. With expert semantic segmentation, data can be organized in multiple formats for AI models across a variety of use cases.
Skeletal annotation
ARSRV teams label images and videos in 360-degree visibility, captured by multi-sensor cameras, in order to build accurate, high-quality, ground truth datasets for use in computer vision models such as autonomous vehicles.
Key points annotation
ARSRV teams outline objects and shape variations by connecting individual points across objects. This annotation type detects body features and could include facial expressions and emotions. Popular use cases for keypoint annotation involve facial recognition.
Lane annotation
ARSRV experts create training datasets using polyline annotation that teach a machine learning model to identify physical boundaries to operate within. Popular use cases include autonomous vehicles and teaching them road boundaries.
Instance segmentation
ARSRV annotators classify images or objects within images based on custom multi-level taxonomies, including land use, crops, residential property features, among others. Expert image classification turns image data into image insights for AI and ML models.
Bitmask annotation
ARSRV annotators classify images or objects within images based on custom multi-level taxonomies, including land use, crops, residential property features, among others. Expert image classification turns image data into image insights for AI and ML models.
Custom annotation
ARSRV’s image annotation platform utilizes image interpolation to rapidly annotate suitable files including JPG, PNG, and even CSV. ARSRV annotation experts create best-in-class video training datasets in rapid time for any AI or ML project. Give your data science team the expert service they need to take their project from idea to production.
Image Annotation Process
Expert Consultation
Transformative, solution-based approach. Interdisciplinary video annotation problem solving. Agility and responsiveness, time-to-value enhancers.
Training
Targeted resources. Custom skilling. Focused and deep microlearning curriculum. Domain expertise. Rostering tools.
Workflow Customization
Alignment of video annotation tools and processes. Structured Development Milestones. Two-step production and QA annotation workflows.
Feedback Cycle
Transparency via analytics. Real time monitoring and service delivery insights. Edge case Insights. Dynamic model improvement.
Evaluation
Assessment of deliverable. Appraisal of key metrics, quality control processes. Model reconsideration. Analysis of business outcome.
