Deep learning methods for object detection in smart manufacturing: A survey
Object detection for industrial applications refers to analyzing the captured images and
videos and finding the relationship between the detected objects for better optimization, data …
videos and finding the relationship between the detected objects for better optimization, data …
Behind the curtain: Learning occluded shapes for 3d object detection
Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding.
However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5 D as they …
However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5 D as they …
A systematic review of object detection from images using deep learning
J Kaur, W Singh - Multimedia Tools and Applications, 2024 - Springer
The development of object detection has led to huge improvements in human interaction
systems. Object detection is a challenging task because it involves many parameters …
systems. Object detection is a challenging task because it involves many parameters …
Centernet-auto: A multi-object visual detection algorithm for autonomous driving scenes based on improved centernet
H Wang, Y Xu, Z Wang, Y Cai… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the rise in popularity of autonomous driving, the speed and accuracy of surrounding
objects' detection by in-vehicle sensing technology is becoming increasingly important for …
objects' detection by in-vehicle sensing technology is becoming increasingly important for …
Coco-o: A benchmark for object detectors under natural distribution shifts
Practical object detection application can lose its effectiveness on image inputs with natural
distribution shifts. This problem leads the research community to pay more attention on the …
distribution shifts. This problem leads the research community to pay more attention on the …
Using context-guided data augmentation, lightweight CNN, and proximity detection techniques to improve site safety monitoring under occlusion conditions
Automated recognition of image patterns in surveillance cameras is beneficial for safety
monitoring. A representative application is visual proximity detection for accident prevention …
monitoring. A representative application is visual proximity detection for accident prevention …
A tri-layer plugin to improve occluded detection
Detecting occluded objects still remains a challenge for state-of-the-art object detectors. The
objective of this work is to improve the detection for such objects, and thereby improve the …
objective of this work is to improve the detection for such objects, and thereby improve the …
Image amodal completion: A survey
Existing computer vision systems can compete with humans in understanding the visible
parts of objects, but still fall far short of humans when it comes to depicting the invisible parts …
parts of objects, but still fall far short of humans when it comes to depicting the invisible parts …
A survey of synthetic data augmentation methods in machine vision
A Mumuni, F Mumuni, NK Gerrar - Machine Intelligence Research, 2024 - Springer
The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …
neural network (CNN) models using large-scale image datasets that are representative of …
深度学习应用于遮挡目标检测算法综述.
孙方伟， 李承阳， 谢永强， 李忠博… - Journal of Frontiers of …, 2022 - search.ebscohost.com
遮挡目标检测长期以来是计算机视觉中的一个难点和研究热点. 目前的深度学习基于卷积神经
网络, 将目标检测任务作为分类任务和回归任务来处理. 当目标被遮挡时, 遮挡物会混淆目标之间 …
网络, 将目标检测任务作为分类任务和回归任务来处理. 当目标被遮挡时, 遮挡物会混淆目标之间 …