Autonomous vehicles are still striving towards full autonomy, and to achieve this goal, they require a wide range of machine learning processes. This can be achieved by training models with a huge volume of different types of training data, which must be tagged for specific purposes.
In the automotive industry, data annotation plays a crucial role in achieving precision and accuracy in autonomous driving. The primary goal of data annotation is to classify and segment objects in images and videos, allowing autonomous vehicles to detect obstacles, recognize road lanes, and understand road signage. Let’s take a look at the most common use cases for data annotation in this area:
Object and Vehicle Detection
Object and vehicle detection is a critical function of autonomous vehicles. To achieve this, various types of annotation are required to train the object detection model, enabling the vehicle to identify persons, vehicles, and other obstacles in its path.
Understanding Signage
The ability to recognize road signs and signals is essential for autonomous vehicles to make safe and accurate decisions. Annotation services play a vital role in enabling this use case by providing careful video labeling that allows the vehicle to automatically detect road signs and respond accordingly.
Environmental Perception
Annotators use semantic segmentation techniques to create training data that labels every pixel in a video frame, allowing the vehicle to understand its surroundings in detail. This complete understanding is crucial for the safe navigation of autonomous vehicles.
Lane Detection
To avoid accidents, autonomous vehicles must recognize road lanes and stay within them. Annotators support this capability by locating road markings in video frames, enabling the vehicle to stay on the right track.
Autonomous vehicles generate a vast amount of data from sensors and cameras, which cannot be used effectively unless they are correctly labeled. Let’s review common types of image annotation with the challenges they have and recommendations to overcome them:
2D bounding boxes
2D bounding box annotation is the simplest data annotation type with the lowest cost. It is rectangular, making it preferred in less complex cases or when budgets are restricted. The annotation technique maps objects in a given image/video to build datasets, allowing ML models to identify and localize objects. Complex objects: Autonomous vehicles need to identify and avoid complex objects like bicycles, pedestrians, and animals. These objects can be challenging to annotate accurately as they have different shapes, sizes, and movements. One way to overcome the challenge of complex objects is to use multiple annotation techniques. Combining bounding boxes with other annotation techniques like semantic segmentation can improve accuracy and reduce false positives.
Inconsistency: The annotation process involves multiple annotators, which can lead to inconsistencies in labeling, making it difficult for the machine learning algorithm to learn from the data.
To address inconsistency, implementing quality control measures like double labeling and inter-annotator agreement can help ensure labeling consistency.
Time-consuming: Annotating large datasets can be a time-consuming and labor-intensive process, which can affect the speed of development.
To speed up the annotation process, automated tools like machine learning-based object detection can be used to pre-label some of the data. This can reduce the workload of human annotators and improve the speed of development.
3D cuboid
3D cuboid annotation involves annotators drawing boxes around objects in an image and is used to judge the distance of the object from the car based on the depth and detecting the object’s volume and position. Complex Shapes and Sizes: Objects in the real world come in various shapes and sizes. Annotating them with 3D cuboids requires expertise and experience. It becomes even more challenging when dealing with complex shapes such as vehicles and pedestrians.
One way to fix this is to use machine learning algorithms that can automatically detect and segment objects in 3D space. Another way is to train the annotators to identify different shapes and sizes of objects accurately.
Occlusion: Occlusion occurs when an object is partially or fully hidden by another object, making it challenging to annotate. This is a common problem in urban environments where buildings, trees, and other objects obstruct the view. To address this problem you can use multi-sensor fusion to combine data from different sensors, such as LiDAR and cameras. This approach can help create a more comprehensive and accurate 3D representation of the environment.
Semantic segmentation
Semantic segmentation assigns a class to each pixel in an image, making it far more accurate than other methods. It deals with dividing objects into groups like bicycles, people, autos, walkways, traffic signals, etc.
Complex object recognition: Semantic segmentation requires annotators to label every pixel in an image or video frame, which can be challenging when the object is complex, such as a tree or a building. Annotators can use various techniques such as polygon annotation, which enables them to label complex objects with precision.
Occlusion: Sometimes, objects may be partially obscured or hidden by other objects in the frame, making it difficult to annotate. Annotators can use 3D point cloud data to fill in the gaps and provide a more complete view of the scene, which can help in segmenting the objects.
Polygon annotation
Polygon annotation is used to annotate irregular shapes like people, animals, and bicycles that require additional details such as the sides of a road, sidewalk, and obstructions, making it a valuable tool for algorithms employed in autonomous vehicles. Complex object shapes: One of the main challenges of polygon annotation is dealing with complex object shapes. Pedestrians and bicyclists, for example, can have a variety of shapes and sizes that can be difficult to label accurately. To simplify the process, it's important to use advanced annotation tools, such as semi-automatic polygon tools or deep learning algorithms.
Occluded objects: Objects can be partially hidden behind other objects or by shadows, making it difficult to accurately annotate them. It's important to use high-quality images and videos that provide clear visibility of the objects. It's also important to use multiple viewpoints and camera angles to get a better understanding of the objects. Consistency: Consistency in labeling is crucial in autonomous vehicle applications. Inaccurate or inconsistent labeling can lead to errors in object detection and classification, which can be dangerous in real-world scenarios. To fix such issues, it's important to have well-defined annotation guidelines and quality control processes in place. Annotators should be trained to follow the guidelines and the annotations should be checked by multiple reviewers to ensure consistency. Using automated tools for quality control can also help improve consistency and reduce errors.
Lines and splines annotation
Lines and splines annotation is used to train models on boundaries and lanes, which allows the car to identify or recognize lanes, enabling the vehicle to move through traffic with ease while still maintaining lane discipline and preventing accidents. Accuracy: Annotation of lines and splines requires high accuracy, as even small mistakes can lead to a malfunction of the autonomous vehicle. However, achieving high accuracy can be challenging due to the complex nature of roadways and different lighting conditions that can affect the visibility of lane markings. Using high-quality annotation tools that provide clear and detailed images of the roadways can also help annotators achieve high accuracy.
Consistency: Maintaining consistency in the annotation of lines and splines can be challenging, as different annotators may use different techniques and standards. Inconsistency in an annotation can affect the accuracy and reliability of autonomous driving systems.
It is important to establish clear annotation guidelines and standards that all annotators must follow. Regular training and monitoring of annotators can also help maintain consistency in the annotation process. Additionally, using advanced annotation tools that provide automated quality control and verification can help ensure that annotations are consistent and accurate.
Video annotation
Video annotation is used to identify and track objects over a collection of frames, with annotations placed on the target object in each frame. The purpose of video annotation is to train predictive algorithms for automated driving. In complicated situations, single-frame annotation is employed since it can ensure quality. Machine learning-based object tracking algorithms have already helped in video annotation, where the initial frame’s objects are annotated by the annotator, and the following frame items are tracked by the algorithm. Complex labeling requirements: Video annotation for autonomous vehicles often requires complex labeling requirements such as object detection, semantic segmentation, and trajectory annotation. Companies can use a combination of manual and automated annotation techniques, leverage advanced annotation tools and software, and hire experienced annotators who are familiar with complex labeling requirements to ensure the effectiveness of the annotation processes.
Large amounts of data: Autonomous vehicles generate vast amounts of data, making it difficult and time-consuming to annotate. Companies can leverage technologies such as machine learning and artificial intelligence to automate the annotation process, reducing the time and effort required. Data annotation plays a critical role in enabling autonomous vehicles to navigate safely and accurately. By providing precise and accurate training data, annotation services can help achieve the full potential of autonomous driving.