arXiv subjects

Irfan Hussain

Publications and source records attributed to Irfan Hussain.

52 records · Page 3Linked to original sources

arXiv2024

Long-Range Vision-Based UAV-assisted Localization for Unmanned Surface Vehicles

The global positioning system (GPS) has become an indispensable navigation method for field operations with unmanned surface vehicles (USVs) in marine environments. However, GPS may not always be available outdoors because it is vulnerable to natural interference and malicious jamming attacks. Thus, an alternative navigation system is required when the use of GPS is restricted or prohibited. To this end, we present a novel method that utilizes an Unmanned Aerial Vehicle (UAV) to assist in localizing USVs in GNSS-restricted marine environments. In our approach, the UAV flies along the shoreline at a consistent altitude, continuously tracking and detecting the USV using a deep learning-based approach on camera images. Subsequently, triangulation techniques are applied to estimate the USV's position relative to the UAV, utilizing geometric information and datalink range from the UAV. We propose adjusting the UAV's camera angle based on the pixel error between the USV and the image center throughout the localization process to enhance accuracy. Additionally, visual measurements are integrated into an Extended Kalman Filter (EKF) for robust state estimation. To validate our proposed method, we utilize a USV equipped with onboard sensors and a UAV equipped with a camera. A heterogeneous robotic interface is established to facilitate communication between the USV and UAV. We demonstrate the efficacy of our approach through a series of experiments conducted during the ``Muhammad Bin Zayed International Robotic Challenge (MBZIRC-2024)'' in real marine environments, incorporating noisy measurements and ocean disturbances. The successful outcomes indicate the potential of our method to complement GPS for USV navigation.

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arXiv2024

Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models

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 Neural Networks (CNNs) and Transformers being employed to capture various aspects of human motion. One of the key contributions of this work is the demonstration of the effectiveness of feature fusion in improving HAR accuracy by capturing spatial and temporal features, which has important implications for the development of more accurate and robust activity recognition systems. The study uses sensory data from HuGaDB, PKU-MMD, LARa, and TUG datasets. Two model, the PO-MS-GCN and a Transformer were trained and evaluated, with PO-MS-GCN outperforming state-of-the-art models. HuGaDB and TUG achieved high accuracies and f1-scores, while LARa and PKU-MMD had lower scores. Feature fusion improved results across datasets.

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arXiv2024

Object Manipulation in Marine Environments using Reinforcement Learning

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 proposes a robust solution for object manipulation from a dock in the presence of disturbances caused by sea waves. To tackle this challenging problem, we apply a deep reinforcement learning (DRL) based algorithm called Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors learn a policy that minimizes an objective function while the critic evaluates the learned policy and provides feedback to guide the actor-learning process. We trained the agent using the PyBullet dynamic simulator and tested it in a realistic simulation environment called MBZIRC maritime simulator. This simulator allows the simulation of different wave conditions according to the World Meteorological Organization (WMO) sea state code. Simulation results demonstrate a high success rate in retrieving the objects from the dock. The trained agent achieved an 80 percent success rate when applied in the simulation environment in the presence of waves characterized by sea state 2, according to the WMO sea state code

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arXiv2024

Conditional Variational Auto Encoder Based Dynamic Motion for Multi-task Imitation Learning

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 to multi-task at the same time. However, those methods require a large number of training data and have limited generalization of the learned behavior to the untrained state. In this paper, we propose a framework that combines the advantages of the traditional DMP-based method and conditional variational auto-encoder (CVAE). The encoder and decoder are made of a dynamic system and deep neural network. Deep neural networks are used to generate torque conditioned on the task ID. Then, this torque is used to create the desired trajectory in the dynamic system based on the final state. In this way, the generated tractory can adjust to the new goal position. We also propose a finetune method to guarantee the via-point constraint. Our model is trained on the handwriting number dataset and can be used to solve robotic tasks -- reaching and pushing directly. The proposed model is validated in the simulation environment. The results show that after training on the handwriting number dataset, it achieves a 100\% success rate on pushing and reaching tasks.

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arXiv2024

Robust Collision Detection for Robots with Variable Stiffness Actuation by Using MAD-CNN: Modularized-Attention-Dilated Convolutional Neural Network

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 several learning-based collision detection methods have been introduced as alternatives to purely model-based detection techniques, there is currently a lack of such methods designed for collaborative robots equipped with variable stiffness actuators. Moreover, there is potential for further enhancing the network's robustness and improving the efficiency of data training. In this paper, we propose a new network, the Modularized Attention-Dilated Convolutional Neural Network (MAD-CNN), for collision detection in robots equipped with variable stiffness actuators. Our model incorporates a dual inductive bias mechanism and an attention module to enhance data efficiency and improve robustness. In particular, MAD-CNN is trained using only a four-minute collision dataset focusing on the highest level of joint stiffness. Despite limited training data, MAD-CNN robustly detects all collisions with minimal detection delay across various stiffness conditions. Moreover, it exhibits a higher level of collision sensitivity, which is beneficial for effectively handling false positives, which is a common issue in learning-based methods. Experimental results demonstrate that the proposed MAD-CNN model outperforms existing state-of-the-art models in terms of collision sensitivity and robustness.

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arXiv2024

Tomato Maturity Recognition with Convolutional Transformers

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that combines the strengths of convolutional neural networks (CNNs) and transformers. Additionally, this study introduces a new tomato dataset named KUTomaData, explicitly designed to train deep-learning models for tomato segmentation and classification. KUTomaData is a compilation of images sourced from a greenhouse in the UAE, with approximately 700 images available for training and testing. The dataset is prepared under various lighting conditions and viewing perspectives and employs different mobile camera sensors, distinguishing it from existing datasets. The contributions of this paper are threefold:Firstly, the authors propose a novel method for tomato maturity classification using a modular convolutional transformer. Secondly, the authors introduce a new tomato image dataset that contains images of tomatoes at different maturity levels. Lastly, the authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification. The effectiveness of the proposed framework in handling cluttered and occluded tomato instances was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno Annotated Tomato, as benchmarks. The evaluation results across these three datasets demonstrate the exceptional performance of our proposed framework, surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated Tomato, respectively.

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arXiv2023

Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer

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 strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields.

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arXiv2023

MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility

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 Generative Adversarial Networks (GANs) and Multi-Level Attention mechanisms for comprehensive underwater image enhancement. The integration of Multi-Level Attention within the GAN architecture significantly enhances the model's capacity to learn discriminative features crucial for precise image restoration. By selectively focusing on relevant spatial and multi-level features, our model excels in capturing and preserving intricate details in underwater imagery, essential for various applications. Extensive qualitative and quantitative analyses on diverse datasets, including UIEB test dataset, UIEB challenge dataset, U45, and UCCS dataset, highlight the superior performance of MuLA-GAN compared to existing state-of-the-art methods. Experimental evaluations on a specialized dataset tailored for bio-fouling and aquaculture applications demonstrate the model's robustness in challenging environmental conditions. On the UIEB test dataset, MuLA-GAN achieves exceptional PSNR (25.59) and SSIM (0.893) scores, surpassing Water-Net, the second-best model, with scores of 24.36 and 0.885, respectively. This work not only addresses a significant research gap in underwater image enhancement but also underscores the pivotal role of Multi-Level Attention in enhancing GANs, providing a novel and comprehensive framework for restoring underwater image quality.

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arXiv2023

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 tailored for diverse underwater scenarios. MARS integrates Residual Attention YOLOv3 with Domain-Adaptive Multi-Scale Attention (DAMSA) to enhance detection accuracy and adapt to different domains. During training, DAMSA introduces domain class-based attention, enabling the model to emphasize domain-specific features. Our comprehensive evaluation across various underwater datasets demonstrates MARS's performance. On the original dataset, MARS achieves a mean Average Precision (mAP) of 58.57\%, showcasing its proficiency in detecting critical underwater objects like echinus, starfish, holothurian, scallop, and waterweeds. This capability holds promise for applications in marine robotics, marine biology research, and environmental monitoring. Furthermore, MARS excels at mitigating domain shifts. On the augmented dataset, which incorporates all enhancements (+Domain +Residual+Channel Attention+Multi-Scale Attention), MARS achieves an mAP of 36.16\%. This result underscores its robustness and adaptability in recognizing objects and performing well across a range of underwater conditions. The source code for MARS is publicly available on GitHub at https://github.com/LyesSaadSaoud/MARS-Object-Detection/

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arXiv2023

ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments

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 environments. The first key contribution is Residual Attention YOLOv3, a novel variant of the YOLOv3 framework empowered by residual attention modules. These modules enable the model to focus on informative features while suppressing background noise, leading to improved detection accuracy and adaptability to different domains. The second contribution is the attention-based domain classification module, vital during training. This module helps the model identify domain-specific information, facilitating the learning of domain-invariant features. Consequently, ADOD can generalize effectively to underwater environments with distinct visual characteristics. Extensive experiments on diverse underwater datasets demonstrate ADOD's superior performance compared to state-of-the-art domain generalization methods, particularly in challenging scenarios. The proposed model achieves exceptional detection performance in both seen and unseen domains, showcasing its effectiveness in handling domain shifts in underwater object detection tasks. ADOD represents a significant advancement in adaptive object detection, providing a promising solution for real-world applications in underwater environments. With the prevalence of domain shifts in such settings, the model's strong generalization ability becomes a valuable asset for practical underwater surveillance and marine research endeavors.

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arXiv2023

Marine$\mathcal{X}$: Design and Implementation of Unmanned Surface Vessel for Vision Guided Navigation

Marine robots, particularly Unmanned Surface Vessels (USVs), have gained considerable attention for their diverse applications in maritime tasks, including search and rescue, environmental monitoring, and maritime security. This paper presents the design and implementation of a USV named marine$\mathcal{X}$. The hardware components of marine$\mathcal{X}$ are meticulously developed to ensure robustness, efficiency, and adaptability to varying environmental conditions. Furthermore, the integration of a vision-based object tracking algorithm empowers marine$\mathcal{X}$ to autonomously track and monitor specific objects on the water surface. The control system utilizes PID control, enabling precise navigation of marine$\mathcal{X}$ while maintaining a desired course and distance to the target object. To assess the performance of marine$\mathcal{X}$, comprehensive testing is conducted, encompassing simulation, trials in the marine pool, and real-world tests in the open sea. The successful outcomes of these tests demonstrate the USV's capabilities in achieving real-time object tracking, showcasing its potential for various applications in maritime operations.

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arXiv2023

Improving Knee Joint Angle Prediction through Dynamic Contextual Focus and Gated Linear Units

Accurate knee joint angle prediction is crucial for biomechanical analysis and rehabilitation. In this study, we introduce FocalGatedNet, a novel deep learning model that incorporates Dynamic Contextual Focus (DCF) Attention and Gated Linear Units (GLU) to enhance feature dependencies and interactions. Our model is evaluated on a large-scale dataset and compared to established models in multi-step gait trajectory prediction. Our results reveal that FocalGatedNet outperforms existing models for long-term prediction lengths (20 ms, 60 ms, 80 ms, and 100 ms), demonstrating significant improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Specifically for the case of 80 ms, FocalGatedNet achieves a notable MAE reduction of up to 24\%, RMSE reduction of up to 14\%, and MAPE reduction of up to 36\% when compared to Transformer, highlighting its effectiveness in capturing complex knee joint angle patterns. Moreover, FocalGatedNet maintains a lower computational load than most equivalent deep learning models, making it an efficient choice for real-time biomechanical analysis and rehabilitation applications.

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arXiv2023

TempoNet: Empowering long-term Knee Joint Angle Prediction with Dynamic Temporal Attention in Exoskeleton Control

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 learning models for gait prediction mainly focus on short-term predictions, leaving the long-term performance of these models relatively unexplored. In this study, we present TempoNet, a novel model specifically designed for precise knee joint angle prediction. By harnessing dynamic temporal attention within the Transformer-based architecture, TempoNet surpasses existing models in forecasting knee joint angles over extended time horizons. Notably, our model achieves a remarkable reduction of 10\% to 185\% in Mean Absolute Error (MAE) for 100 ms ahead forecasting compared to other transformer-based models, demonstrating its effectiveness. Furthermore, TempoNet exhibits further reliability and superiority over the baseline Transformer model, outperforming it by 14\% in MAE for the 200 ms prediction horizon. These findings highlight the efficacy of TempoNet in accurately predicting knee joint angles and emphasize the importance of incorporating dynamic temporal attention. TempoNet's capability to enhance knee joint angle prediction accuracy opens up possibilities for precise control, improved rehabilitation outcomes, advanced sports performance analysis, and deeper insights into biomechanical research. Code implementation for the TempoNet model can be found in the GitHub repository: https://github.com/LyesSaadSaoud/TempoNet.

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arXiv2023

Evaluating Deep Learning Assisted Automated Aquaculture Net Pens Inspection Using ROV

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 result in significant financial losses and compromise the nearby marine ecosystem. The traditional inspection system in use relies on visual inspection by expert divers or ROVs, which is not only laborious, time-consuming, and inaccurate but also largely dependent on the level of knowledge of the operator and has a poor degree of verifiability. This article presents a robotic-based automatic net defect detection system for aquaculture net pens oriented to on-ROV processing and real-time detection. The proposed system takes a video stream from an onboard camera of the ROV, employs a deep learning detector, and segments the defective part of the image from the background under different underwater conditions. The system was first tested using a set of collected images for comparison with the state-of-the-art approaches and then using the ROV inspection sequences to evaluate its effectiveness in real-world scenarios. Results show that our approach presents high levels of accuracy even for adverse scenarios and is adequate for real-time processing on embedded platforms.

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arXiv2023

Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions

Visual perception is an important component for autonomous navigation of unmanned surface vessels (USV), particularly for the tasks related to autonomous inspection and tracking. These tasks involve vision-based navigation techniques to identify the target for navigation. Reduced visibility under extreme weather conditions in marine environments makes it difficult for vision-based approaches to work properly. To overcome these issues, this paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework consists of an integrated perception pipeline that uses a generative adversarial network (GAN) to remove noise and highlight the object features before passing them to the object detector (i.e., YOLOv5). The detected visual features are then used by the USV to track the target. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset, on which the proposed scheme has outperformed the existing methods across various metrics.

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arXiv2022

Novel Supernumerary Robotic Limb based on Variable Stiffness Actuators for Hemiplegic Patients Assistance

Loss of upper extremity motor control and function is an unremitting symptom in post-stroke patients. This would impose hardships on accomplishing their daily life activities. Supernumerary robotic limbs (SRLs) were introduced as a solution to regain the lost Degrees of Freedom (DoFs) by introducing an independent new limb. The actuation systems in SRL can be categorized into rigid and soft actuators. Soft actuators have proven advantageous over their rigid counterparts through intrinsic safety, cost, and energy efficiency. However, they suffer from low stiffness, which jeopardizes their accuracy. Variable Stiffness Actuators (VSAs) are newly developed technologies that have been proven to ensure accuracy and safety. In this paper, we introduce the novel Supernumerary Robotic Limb based on Variable Stiffness Actuators. Based on our knowledge, the proposed proof-of-concept SRL is the first that utilizes Variable Stiffness Actuators. The developed SRL would assist post-stroke patients in bi-manual tasks, e.g., eating with a fork and knife. The modeling, design, and realization of the system are illustrated. The proposed SRL was evaluated and verified for its accuracy via predefined trajectories. The safety was verified by utilizing the momentum observer for collision detection, and several post-collision reaction strategies were evaluated through the Soft Tissue Injury Test. The assistance process is qualitatively verified through standard user-satisfaction questionnaire.

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