Figure 2 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of such detection frameworks in specific applications such as autonomous driving is yet an area to be addressed. This study presents a...
Contexts in source publication
Context 1
... different classes are considered for this problem: vehicles (which includes any wheeled motor object such as cars or motorbikes), pedestrians and cyclists. Figure 2a shows an example of the labeled data provided, which are tightly fitting bounding boxes around the objects. Furthermore, Waymo provides two different difficulty levels for the labels (Level 1 and 2), which are illustrated in Figure 2b. ...
Context 2
... 2a shows an example of the labeled data provided, which are tightly fitting bounding boxes around the objects. Furthermore, Waymo provides two different difficulty levels for the labels (Level 1 and 2), which are illustrated in Figure 2b. Level 2 instances are objects considered as hard and the criteria depends on both the human labelers and the object statistics. ...
Similar publications
Human detection methods are widely used in various fields such as autonomous vehicles, video surveillance, and rescue systems. To provide a more effective detection system, different types of sensor data (i.e. optics, thermal, and depth data) may be used together as hybrid information. Fortifying object detection, based on optical data and addition...
In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to detect objects, namely lost/forgotten items to inform the passengers, and aggressive items to monitoring if violent actions may arise between passengers. For object...
Obstacle detection on road is a challenging task in autonomous vehicle driving. Although obstacle detection is carried out with the help of sensors which are accurate and precise in real-time, they are not cost effective and computationally intensive. So, a computer vision and deep learning-based approach can be considered as a potential alternativ...
Autonomous vehicles (AVs) are the future of transportation and are likely to bring countless benefits compared to human-operated driving. However, there are still a lot of advances to be made before these vehicles can be considered completely safe and before they can reach full autonomy. Perceiving the environment with the utmost accuracy and speed...
In the systems of industrial robotics and autonomous vehicles, instance segmentation is widely employed. However, manually labelling an object outline is time‐consuming. In order to reduce annotation costs, we present a weakly supervised instance segmentation method in this article. A deeply convolutional network is first used to construct multi‐sc...
