A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal
Although great progress has been made in generic object detection by advanced deep
learning techniques, detecting small objects from images is still a difficult and challenging …
learning techniques, detecting small objects from images is still a difficult and challenging …
Road infrastructure challenges faced by automated driving: A review
Automated driving can no longer be referred to as hype or science fiction but rather a
technology that has been gradually introduced to the market. The recent activities of …
technology that has been gradually introduced to the market. The recent activities of …
A guide to image and video based small object detection using deep learning: Case study of maritime surveillance
Small object detection (SOD) in optical images and videos is a challenging problem that
even state-of-the-art generic object detection methods fail to accurately localize and identify …
even state-of-the-art generic object detection methods fail to accurately localize and identify …
Automatic traffic sign detection and recognition using SegU-Net and a modified Tversky loss function with L1-constraint
Traffic sign detection is a central part of autonomous vehicle technology. Recent advances
in deep learning algorithms have motivated researchers to use neural networks to perform …
in deep learning algorithms have motivated researchers to use neural networks to perform …
Traffic sign detection under challenging conditions: A deeper look into performance variations and spectral characteristics
Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we
need to carefully assess the capabilities and limitations of automated traffic sign detection …
need to carefully assess the capabilities and limitations of automated traffic sign detection …
Neural-network-based traffic sign detection and recognition in high-definition images using region focusing and parallelization
Recent trends in the development of autonomous vehicles focus on real-time processing of
vast amounts of data from various sensors. The data can be acquired using multiple …
vast amounts of data from various sensors. The data can be acquired using multiple …
Evaluation method of deep learning-based embedded systems for traffic sign detection
M Lopez-Montiel, U Orozco-Rosas… - IEEE …, 2021 - ieeexplore.ieee.org
Traffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous
vehicles; it is one of the most critical visual perception problems since failing in this task may …
vehicles; it is one of the most critical visual perception problems since failing in this task may …
Robustness of object detectors in degrading weather conditions
State-of-the-art object detection systems for autonomous driving achieve promising results in
clear weather conditions. However, such autonomous safety critical systems also need to …
clear weather conditions. However, such autonomous safety critical systems also need to …
A survey of FPGA-based vision systems for autonomous cars
On the road to making self-driving cars a reality, academic and industrial researchers are
working hard to continue to increase safety while meeting technical and regulatory …
working hard to continue to increase safety while meeting technical and regulatory …
Robustness of visual perception system in progressive challenging weather scenarios
X Li, S Zhang, X Chen, Y Wang, Z Fan, X Pang… - … Applications of Artificial …, 2023 - Elsevier
Traditional field test and laboratory test can only evaluate hardware performance, and
cannot test the robustness of artificial intelligence (AI) device for object detection, instance …
cannot test the robustness of artificial intelligence (AI) device for object detection, instance …