Zobrazeno 1 - 10
of 220
pro vyhledávání: '"Hussain, Irfan"'
Autor:
Belal, Mohammad, Hassan, Taimur, Ahmed, Abdelfatah, Aljarah, Ahmad, Alsheikh, Nael, Hussain, Irfan
Human activity recognition (HAR) is a crucial area of research that involves understanding human movements using computer and machine vision technology. Deep learning has emerged as a powerful tool for this task, with models such as Convolutional Neu
Externí odkaz:
http://arxiv.org/abs/2406.16638
Publikováno v:
15th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles (CAMS 2024)
Performing intervention tasks in the maritime domain is crucial for safety and operational efficiency. The unpredictable and dynamic marine environment makes the intervention tasks such as object manipulation extremely challenging. This study propose
Externí odkaz:
http://arxiv.org/abs/2406.03223
The dynamic motion primitive-based (DMP) method is an effective method of learning from demonstrations. However, most of the current DMP-based methods focus on learning one task with one module. Although, some deep learning-based frameworks can learn
Externí odkaz:
http://arxiv.org/abs/2405.15266
Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management
Externí odkaz:
http://arxiv.org/abs/2312.16331
Autor:
Bakht, Ahsan Baidar, Jia, Zikai, Din, Muhayy ud, Akram, Waseem, Soud, Lyes Saad, Seneviratne, Lakmal, Lin, Defu, He, Shaoming, Hussain, Irfan
The underwater environment presents unique challenges, including color distortions, reduced contrast, and blurriness, hindering accurate analysis. In this work, we introduce MuLA-GAN, a novel approach that leverages the synergistic power of Generativ
Externí odkaz:
http://arxiv.org/abs/2312.15633
MARS: Multi-Scale Adaptive Robotics Vision for Underwater Object Detection and Domain Generalization
Underwater robotic vision encounters significant challenges, necessitating advanced solutions to enhance performance and adaptability. This paper presents MARS (Multi-Scale Adaptive Robotics Vision), a novel approach to underwater object detection ta
Externí odkaz:
http://arxiv.org/abs/2312.15275
This research presents ADOD, a novel approach to address domain generalization in underwater object detection. Our method enhances the model's ability to generalize across diverse and unseen domains, ensuring robustness in various underwater environm
Externí odkaz:
http://arxiv.org/abs/2312.06801
Ensuring safety is paramount in the field of collaborative robotics to mitigate the risks of human injury and environmental damage. Apart from collision avoidance, it is crucial for robots to rapidly detect and respond to unexpected collisions. While
Externí odkaz:
http://arxiv.org/abs/2310.02573
Autor:
Saoud, Lyes Saad, Hussain, Irfan
In the realm of exoskeleton control, achieving precise control poses challenges due to the mechanical delay of exoskeletons. To address this, incorporating future gait trajectories as feed-forward input has been proposed. However, existing deep learn
Externí odkaz:
http://arxiv.org/abs/2310.01795
In marine aquaculture, inspecting sea cages is an essential activity for managing both the facilities' environmental impact and the quality of the fish development process. Fish escape from fish farms into the open sea due to net damage, which can re
Externí odkaz:
http://arxiv.org/abs/2308.13826