Polyline Image Annotation: A Brief Insight

Image annotation establishes the standards that is copied by the model along with any errors in the labels. Hence, accurate image annotation sets down the foundation for the training of neural networks enabling annotation to be among the most important tasks in computer vision. Image annotation can be done manually as well as by using an automated annotation tool.

Automated annotation tools have pre-trained algorithms that can annotate images with a certain level of accuracy. Its annotations are necessary for complicated annotations that involve creation of segment masks.

Various tasks require data which can be annotated in different forms for it to be used directly for training. While basic tasks like classification need data to be annotated simply with simple tags, complex tasks like segmentation and object detection need data that has pixel map annotations and bounding box annotations. The various types of annotations used for various tasks have been listed below:

After manual annotation is complete, the labeled images are processed through a machine learning or deep learning model with the aim of replicating the annotations without human supervision.
Image annotation establishes the standards that is copied by the model along with any errors in the labels. Hence, accurate image annotation sets down the foundation for the training of neural networks enabling annotation to be among the most important tasks in computer vision. Image annotation can be done manually as well as by using an automated annotation tool.

Automated annotation tools have pre-trained algorithms that can annotate images with a certain level of accuracy. Its annotations are necessary for complicated annotations that involve creation of segment masks.

Various tasks require data which can be annotated in different forms for it to be used directly for training. While basic tasks like classification need data to be annotated simply with simple tags, complex tasks like segmentation and object detection need data that has pixel map annotations and bounding box annotations. The various types of annotations used for various tasks have been listed below:

1. Bounding Box
2. Polygon
3. 3D Cuboid
4. Semantic Segmentation
5. Polyline
6. Keypoint

There are several images and video frames at the core of machine learning and computer vision that are interpreted by machines for executing actions. The methodology used for annotating data is very much dependent on the area of study or end goal. Let’s discuss in this blog one of the most popular image annotation technique i.e. polyline image annotation.

What is Polyline Image Annotation?

Polylines are used when roads and pathways are the central focus of your AI model. Polylines are linear lines that can be drawn to trace road and pathways connecting at independent vertices. Splines are used in instances where the lines are not straight but have mild curves. It helps in outlining markings which are curvier in nature. It is very much similar to polyline and is made with the help of same tools. The only distinction between the two is that splines can be bent for outlining a curved line via a selection tool.

One of the most popular use case of polyline annotation is self-driving cars or autonomous vehicles. There are many other use cases of polyline annotation like agriculture, robotics, lane detection by autonomous vehicles. Self-driving cars or autonomous vehicles have been around for a long time, however there are many ifs and buts surrounding it.

Let’s read more about how this technique in this blog.

How is polyline annotation used across industries?

1. Road markings: Drivers must pay heed to signs and symbols on the road to keep themselves and others safe. In case of autonomous vehicles, they must be aware of basic but most important road safety rules for preventing collisions. A knowledge of markings and symbols on the road can potentially prevent a crash and ensure a smooth ride. Machines have to be taught how to understand bike lanes, bus stops, and crosswalks. Also, it’s a fact that there different cities and countries are run by different traffic laws. Your AI model should be able to understand these differences.
2. Lane Detection: Similar to road markings, autonomous vehicles should also be able to identify lanes in the road. While the two lines in the center of the road divide traffic, the dotted white lines indicate a one-way. With car companies becoming familiar with AI, drivers are able to reap the benefits of technology which helps them in staying in their lane and not drift. Since lanes are straight, polylines are ideal for annotation. Also, tracing the lane direction for the annotator is easier and most effective.
3. Obstacle Detection: There are many obstacles on the street and form part of the driving experience. We are used to dealing with these things as seasoned drivers. Autonomous vehicles need to be able to identify hurdles on the road to prevent them. Polyline annotation enables a definite way of labeling which demonstrates these road signs without diving into details.
4. Robotics: Industrial robots are used in warehouse for lifting things from one location and moving them to another. Robots are primarily used for restocking items in warehouses as they are efficient and great in saving company’s time and money. Even though bounding boxes might appear to be the right annotation technique for building this type of model, polylines are a great alternative as they create a target zone between two lines for placing the objects.
5. Agriculture: Polyline annotation is used in crop farming for automating agricultural processes like monitoring crops, irrigation, tracking the presence of insects for pest control resulting in higher productivity and yield.

Here are four reasons to choose Anolytics for polyline annotation services

Anolytics is among the leading data annotation and labeling experts having significant industry exposure. It is the ideal choice for your AI training data requirements. We offer the following:

1. Quality with Accuracy: Get the best-in-class quality services with highest accuracy level delivering an excellence in image annotation through multiple stages of auditing and reviewing of labeled data.
2. Security with Privacy: We are certified with SOC 2 TYPE 1 Company for maintaining the high standards of data security with privacy while working with our clients to ensure their confidentiality.
3. Fully Scalable Service: Working with hundreds of workforce to annotate pictures as per the demand providing a completely scalable solution with turnaround time to meet the different client needs.
4. Cost-effective Pricing: Image annotation outsourcing to us means our clients get a cost-effective data labeling service helping them to minimize the cost of their project with best efficiency.