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Sonya Coleman

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13 papers

2 author rows

EAAI Journal 2024 Journal Article

Bilateral guidance network for one-shot metal defect segmentation

Dexing Shan
Yunzhou Zhang
Xiaozheng Liu
Jiaqi Zhao
Sonya Coleman
Dermot Kerr

Metal defect inspection is critical for maintaining product quality and ensuring production safety. However, the vast majority of existing defect segmentation methods rely heavily on large-scale datasets that only cater to specific defects, making them unsuitable for the industrial sector, where training samples are often limited. To address these challenges, we propose a bilateral guidance network for one-shot metal defect segmentation that leverages the perceptual consistency of background regions within industrial images to distinguish foreground and background regions. Our model uses an interactive feature reweighting scheme that models the inter- and self-dependence of foreground and background feature maps, enabling us to build robust pixel-level correspondences. Our proposed method demonstrates good domain adaptability and accurately segments defects in multiple materials, such as steel, leather, and carpet, among others. Additionally, we have incorporated a multi-scale receptive field encoder to enhance the model’s ability to perceive objects of varying scales, providing a comprehensive solution for industrial defect segmentation. Experimental results indicate that our proposed method has the potential to be effective in a variety of real-world applications where defects may not be immediately visible or where large amounts of labeled data are not readily available. With only one shot, our method achieves the state-of-the-art performance of 41. 62% mIoU and 70. 30% MPA on the Defect-3 i dataset.

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EAAI Journal 2023 Journal Article

A novel seminar learning framework for weakly supervised salient object detection

Yan Liu
Yunzhou Zhang
Zhenyu Wang
Fei Yang
Feng Qiu
Sonya Coleman
Dermot Kerr

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much attention from several research perspectives, it has revealed two problems while driving the rapid development of saliency detection. (1) Large divergence in the characteristics of saliency regions in terms of location, shape and size makes them difficult to recognize. (2) The properties of convolutional neural networks dictate that it is insensitive to various transformations, which will lead to hardly balance the application of various disturbances. To tackle these limitations, this paper proposes a novel seminar learning framework with consistent transformation ensembling (SLF-CT) for scribble supervised SOD. The framework consists of the teacher–student model and the student–student model for segmenting the salient objects. Specifically, we first design a cross attention guided network (CAGNet) as a baseline model for saliency prediction. Then we assign CAGNet to the teacher–student model, where the teacher network is based on the exponential moving average and guides the training of the student network. Moreover, we adopt multiple pseudo labels to transfer the information among students from different conditions. To further enhance the regularization of the network, a consistency transformation mechanism is also incorporated, which encourages the saliency prediction and input image of the network to be consistent. The experimental results demonstrate that the proposed approach performs favorably comparable with the state-of-the-art weakly supervised methods. As far as we know, the proposed approach is the first application of seminar learning in the SOD area.

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IROS Conference 2023 Conference Paper

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

You Shen
Yunzhou Zhang
Yanmin Wu
Zhenyu Wang 0010
Linghao Yang
Sonya Coleman
Dermot Kerr

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://github.com/mystorm16/BSH-Det3D.

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ICRA Conference 2023 Conference Paper

SAMLoc: Structure-Aware Constraints With Multi-Task Distillation for Long-Term Visual Localization

Jian Ning
Yunzhou Zhang
Xinge Zhao
Sonya Coleman
Kunmo Li
Dermot Kerr

Real-time and robust long-term visual localization is a crucial technology for autonomous driving. Season and illumination variance make this problem more challenging. At present, most of excellent visual localization algorithms cannot run in real-time on devices with limited computing resources. In this paper, we propose SAMLoc, a structure-aware and self-supervised visual localization system, for fast and robust 6-DoF localization. To obtain structural features in the scene, we propose local and global structure-aware constraints using edge information. Then, we integrate the structure-aware constraints into the hierarchical localization network of multi-task distillation, which significantly reduces the feature extraction time while ensuring localization accuracy. As a result, real-time and robust large-scale localization can be achieved on mobile devices. Experimental results on public datasets show that our system can achieve high localization accuracy and have satisfactory real-time performance. Compared with several state-of-the-art visual localization systems, our framework achieves a competitive localization performance.

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IROS Conference 2022 Conference Paper

Semantic Topological Descriptor for Loop Closure Detection within 3D Point Clouds In Outdoor Environment

Ming Liao
Yunzhou Zhang
Jinpeng Zhang
Liang Liang
Sonya Coleman
Dermot Kerr

Loop closure detection has the potential to correct the drift of trajectories and build a global consistent map in LiDAR SLAM, however it remains a challenging problem in outdoor environment due to the sparsity of 3D point clouds data, large-scale scenes and moving objects. Inspired by the way humans perceive the environment through recognizing objects and identifying their relations, this paper presents a novel descriptor that contains semantic and topological information for loop closure detection. Unlike most existing methods that extract features from the raw point clouds or use all semantic objects, we directly discard point clouds representing pedestrians and vehicles after semantic segmentation. Then, we propose a semantic topological graph representation from the remaining point clouds and convert this graph into a descriptor. Additionally, we propose a two-stage algorithm for matching descriptors to efficiently determine the loop. Our method has been extensively evaluated using the KITTI dataset and outperforms state-of-the-art methods, especially in the challenging situations such as viewpoint changes and dynamic scenes.

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ICRA Conference 2021 Conference Paper

Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry

Rui Tian 0002
Yunzhou Zhang
Delong Zhu 0001
Shiwen Liang
Sonya Coleman
Dermot Kerr

Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more promising approach. In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane. The framework includes a ground point extraction algorithm for selecting high-quality points on the ground plane, and a ground point aggregation algorithm for joining the extracted ground points in a local sliding window. Based on the aggregated data, the scale is finally recovered by solving a least-squares problem using a RANSAC-based optimizer. Sufficient data and robust optimizer enable a highly accurate scale recovery. Experiments on the KITTI dataset show that the proposed framework can achieve state-of-the-art accuracy in terms of translation errors, while maintaining competitive performance on the rotation error. Due to the light-weight design, our framework also demonstrates a high frequency of 20 Hz on the dataset.

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IROS Conference 2020 Conference Paper

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Yanmin Wu
Yunzhou Zhang
Delong Zhu 0001
Yonghui Feng
Sonya Coleman
Dermot Kerr

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on https://github.com/yanmin-wu/EAO-SLAM.

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ICRA Conference 2017 Conference Paper

Reliable object handover through tactile force sensing and effort control in the Shadow Robot hand

Augusto Gómez Eguíluz
Iñaki Rañó
Sonya Coleman
T. Martin McGinnity

A fundamental problem in cooperative HumanRobot Interaction is object handover. Existing works in this area assume the human can reliably grasp the object from the robot hand. However, in some situations the human can produce perturbing forces in the object that are not meant to end in a handover. These perturbations can result in the object being dropped or the robot hand being damaged. This paper addresses this problem and presents a mechanism for reliable robot to human object handover implemented in a Shadow Robot hand endowed with tactile sensing. Given a stable grasping configuration, using BioTAC sensors we are able to estimate the contact forces applied to the object, and provide a feedback signal to a joint effort controller to maintain grasp forces despite perturbations. Our system is able to identify between object pulling forces which should result in an object handover, and other disturbances. Experimental results show that the hand releases the object only when the object is pulled, validating the proposed algorithm.

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IROS Conference 2016 Conference Paper

A multi-modal approach to continuous material identification through tactile sensing

Augusto Gómez Eguíluz
Iñaki Rañó
Sonya Coleman
T. Martin McGinnity

Tactile sensing has been used in robotics for object identification, grasping, and material recognition. Most material recognition approaches use vibration signals from a tactile exploration, typically above one second long, to identify the material. This work proposes a tactile multi-modal (vibration and thermal) material identification approach based on recursive Bayesian estimation. Through the frequency response of the vibration induced by the material and thermal features, like an estimate of the thermal power loss of the finger, we show that it is possible to identify materials in less than half a second. Moreover, a comparison between vibration only and multi-modal identification shows that both recognition time and classification errors are reduced by adding thermal information.

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EAAI Journal 2015 Journal Article

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Mauro Dragone
Giuseppe Amato
Davide Bacciu
Stefano Chessa
Sonya Coleman
Maurizio Di Rocco
Claudio Gallicchio
Claudio Gennaro

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user׳s activities and changing user׳s habits.

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JBHI Journal 2015 Journal Article

Temporal Changes of Diffusion Patterns in Mild Traumatic Brain Injury via Group-Based Semi-blind Source Separation

Min Jing
T. Martin McGinnity
Sonya Coleman
Armin Fuchs
J. A. Scott Kelso

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

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IROS Conference 2011 Conference Paper

A fast distributed auction and consensus process using parallel task allocation and execution

Gautham P. Das
T. Martin McGinnity
Sonya Coleman
Laxmidhar Behera

In a multi-robot system, the coordination and cooperation among the robots determine the effectiveness of task execution. Different centralised and distributed task allocation algorithms have been proposed by researchers. Recently consensus based task allocation has been extensively researched because of its robustness in handling large teams of robots. We propose a new auction and consensus based algorithm for fast task allocation in parallel with task execution. The performance of the proposed algorithm under different conditions is analyzed and compared with other distributed consensus algorithms.

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ICRA Conference 2007 Conference Paper

Feature Extraction on Range Images - A New Approach

Sonya Coleman
Bryan W. Scotney
Shanmugalingam Suganthan

Range images can provide an almost 3-dimensional description of a scene. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Feature extraction on range images has proven to be a more complex problem than on intensity images due to both the irregular distribution of range image data and the nature of the features that are present in range images. Approaches to range image feature extraction are often scan line based approximations that carry a significant computational overhead and hence are not appropriate for real-time processing. This paper presents a design procedure for scalable first order derivative operators that can be used directly on irregularly distributed data. Hence the method is appropriate for direct use on range image data without the requirement of image preprocessing and could form the basis of algorithms of real-time robotic applications.

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