You must have passed your driving test years ago, and since then, you can easily read road marks and traffic signs. But you probably wouldn’t mind your lovely car taking you home every day? Or maybe you’re one of those people who think that self-driving cars aren’t safe enough to sit in? Well, then you need to learn more about how cars can understand street signs and identify pedestrians.
In this article, you’ll find out how polyline annotation makes AI algorithms of autonomous vehicles work. And once you need to hire a team of professional annotations, read on!
What Is Polyline Annotation?
With polyline annotation, a labeler marks lines and curved lines (splines) on images and labels them with text (however, don’t confuse the latter with text labeling, which annotators use to tag certain words, phrases, or sentences in a text). In polyline annotation, the text is used to name the lines. And those lines are road markings –– on city streets, roads, highways, routes, etc. But those lines can be marked on a factory floor too.
The goal of line annotation is to teach vehicles to drive on their own. On plants, for example, forklifts can be trained to unpack and move boxes to production halls through specified routes. And autonomous cars learn how to drive on real-world roads because they can identify double, single solid, broken lanes, road edges, and other crucial road marks.
They’ll definitely need to read traffic lights and road signs and detect people and moving objects. But this is the responsibility of other annotation approaches –– bounding boxes, semantic segmentation, 3D sensor fusion data labeling, and others.
Autonomous Vehicle Perception of Road Marks and Surroundings
Cars can possess one of the six levels of autonomy. Those with level 0 are traditional ones, and they require total control of a driver. The 5th level assumes that such cars can be driverless. The maximum level of autonomy current commercial self-driving solutions offer is 3. These cars have speed and steering control, interpret road surface marking, and make independent decisions. But they still require a fully involved driver ready to react to changing traffic situations.
Autonomous vehicles are equipped with cameras, lidar (light detection and ranging) systems, and GPS. Lidar uses lasers to reconstruct the 3D model of its surroundings, and its lasers work faster and more accurately than radars and sonars. There are even solutions with the 4th level of autonomy (like Google’s robotaxi), but these models aren’t yet available for sale due to certain limitations. Surprisingly, 20% of drivers think that fully independent cars are already on sale.
The main constraints are lack of official regulations, no public acceptance, and poorly-trained autonomous vehicle machine learning algorithms. AI still can’t always effectively perform lane lines detection or correctly identify 3D objects.
Tasks That Polyline Annotation Helps to Complete
There are several industries where polyline annotation is actively used.
- For autonomous vehicles lane detection. Though fully autonomous cars aren’t yet here, that’s definitely the most common industry where polyline technique is used. And here, data labelers and computer vision developers face the most responsible task to accomplish. That’s because a proper understanding of road marking by AI is vital for passengers’ safety.
- For identification of crop rows. Polyline annotation is helpful in agriculture when it goes about detecting rows of various plants. It can also help notice broken trees and changed forms of some herbs, fruits, vegetables, etc. Some use cases prove that this technique can detect even leg positions of various insects.
- For accurate lane path detection by industrial robots. Robots can help people move objects once they get clear instructions about the starting and finishing points and the road between the two. Similar to driverless cars, these mechanisms can move on the territory of plants, delivery services, agricultural objects, etc.
- For other purposes. The polyline technique can be used in different industries too. Some of the examples are healthcare, nutrition, drone imagery, etc.
Reasons to Outsource a Polyline Annotation Team with Us
If you need to annotate a self-driving car dataset professionally, we can help you assemble a team of top labelers in a few weeks. And here are the reasons to choose us:
- Lower costs. We work with data annotations from Ukraine, and their salaries are 10-15% lower than those of their colleagues from the USA and Western Europe.
- Better specialists. With us, you get access to an impressive pool of talents so that you can hire more experienced and skilled data annotators for less money.
- More time for work-related tasks. Our format of cooperation assumes that we take care of all salary-related and administrative issues. This leaves you entirely focused on business goals.
- Flexibility. We value our clients’ finances and offer them to pay only for the services they need. Once your AI project is paused, you won’t have to pay for the hired employees.
- A fixed-fee. Instead of several invoices to settle at a month-end, we offer you to pay only one amount. There are no payrolls, rentals, utilities, or admin expenses for you to transfer –– isn’t this great?
How We Hire Lane Detection Deep Learning Professionals
Our recruiting managers use advanced approaches to sourcing candidates. They also leverage referral networks to find the best candidates for your spline annotation team. And that’s how it works:
- You complete the contact form. Our managers will call you back to discuss the requirements for the candidates. Once you approve the job description, we post it to job boards, social media, or our own referral network and start sourcing.
- In 2-4 weeks, you get the first CVs. The candidates you’ll find on the list will be preliminarily interviewed, and our recruiters will leave some notes about them. Once you complete testing and interviewing, you confirm the candidates, sending them pre-approved job offers.
- We do the onboarding as soon as the candidates accept their offers. We also initiate the official hiring procedures in Ukraine to meet all legal and social commitments to employees.