Article
Environmental Sciences
Jing Zhang, Da Xu, Yunsong Li, Liping Zhao, Rui Su
Summary: In this paper, a one-stage end-to-end network called FusionPillars is proposed to fuse multisensor data, including LiDAR point cloud and camera images. FusionPillars includes three branches: a point-based branch, a voxel-based branch, and an image-based branch. Experimental results revealed that, compared to existing one-stage fusion networks, FusionPillars yield superior performance, with a considerable improvement in the detection precision for small objects.
Article
Computer Science, Information Systems
Bangbang Yang, Zhaoyang Huang, Yijin Li, Han Zhou, Hongsheng Li, Guofeng Zhang, Hujun Bao
Summary: In recent years, point cloud registration using deep learning techniques has been successful. However, existing methods based on pure geometric context struggle with sensor noise and geometric ambiguities. To address these issues, we propose a method that learns a 3D hybrid feature by combining multi-view colored images and point clouds. We extract informative 2D features from the images and fuse them with a novel soft-fusion module, improving registration performance on real-world datasets.
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Dongsheng Liu, Yan Tian, Yujie Zhang, Judith Gelernter, Xun Wang
Summary: Tooth point cloud segmentation is crucial in digital dentistry, but has challenges of analyzing heterogeneous geometry data and aligning loss function with evaluation metrics. This paper presents an interacted graph network to address these challenges, and compares experimental results using the Shining3D Tooth Segmentation dataset.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Software Engineering
Sneha Paul, Zachary Patterson, Nizar Bouguila
Summary: This paper presents DualMLP, a novel 3D model that introduces the concept of a two-stream network to handle the trade-off between the number of points and the computational overhead in existing 3D models. By using a small number of points in one branch and a larger number of points in the other branch, DualMLP achieves improved scene understanding while maintaining computational efficiency.
Article
Environmental Sciences
Linlin Zhao, Huirong Zhang, Jasper Mbachu
Summary: This study proposes a workflow for 3D modeling of complex structures using multiple-source data. It utilizes TLS, GNSS/IMU, photogrammetry, UAV platform, computer vision, and AI algorithms. Through the use of deep learning networks for image matching and generating a 3D point cloud, along with GNSS information for accurate transformation, the proposed workflow produces a complete and accurate 3D model with satisfactory accuracy.
Article
Construction & Building Technology
Mojtaba Noghabaei, Yajie Liu, Kevin Han
Summary: This paper presents a general compatibility analysis method for detecting construction incompatibilities in modular construction using reality capture technologies. The proposed method involves scanning the modules in manufacturing plant and construction site, and checking module-to-module compatibility remotely prior to shipment and installation, aiming to avoid project delays and reworks.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Environmental Sciences
Li Yan, Jicheng Dai, Yinghao Zhao, Changjun Chen
Summary: This paper proposes a spinning actuated LiDAR mapping system and a tightly coupled laser-inertial SLAM algorithm. It improves the stability and accuracy of point cloud registration by extracting edge and plane features and using an adaptive scan accumulation method. By adding LiDAR feature factors and IMU pre-integration factors to the factor graph and jointly optimizing them, the trajectory can be output. An improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce drift. Experimental results show that this algorithm is more accurate and achieves real-time performance compared to existing algorithms.
Article
Chemistry, Analytical
Gustavo Scalabrini Sampaio, Leandro A. Silva, Mauricio Marengoni
Summary: The utilization of technology in agriculture, including robotics, field sensors, and computer vision, has led to significant improvements. A system capable of generating 3D models of non-rigid corn plants has been developed, allowing for accurate plant structural measurements and mapping of the plant's environment to enhance crop efficiency.
Article
Computer Science, Artificial Intelligence
Long Xi, Wen Tang, Tao Xue, TaoRuan Wan
Summary: This paper proposes a novel unsupervised deep learning network, named Binary Tree Network (BTreeNet), which effectively aligns partial and noisy 3D point clouds without training. By separating the learning of rotation and translation features, BTreeNet achieves remarkable generalization and robustness to unseen large and dense scenes. Furthermore, the Iterative Binary Tree Network (IBTreeNet) is introduced to continuously improve registration accuracy for such scenarios.
Article
Geography, Physical
Can Pu, Chuanyu Yang, Jinnian Pu, Radim Tylecek, Robert B. Fisher
Summary: This study proposes a solution based on a newly-designed panoramic stereo camera and a hybrid software framework to address the importance of recovering an outdoor environment's surface mesh for an agricultural robot during task planning and remote visualization. The solution includes three fusion modules: disparity fusion, pose fusion, and volumetric fusion. The proposed framework and its three fusion modules demonstrate superior performance in experiments.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Chemistry, Analytical
Saidrasul Usmankhujaev, Shokhrukh Baydadaev, Jang Woo Kwon
Summary: Distance estimation using only a monocular camera is a challenging task in computer vision due to occlusions, noise, lighting variations, and object shape changes. Various techniques, including stereo matching and depth from motion, have been proposed to address these challenges. This paper presents a novel method using converted point cloud data and a map-based bird's eye view approach to calculate distances to detected objects. The proposed method demonstrates good accuracy and robustness, extracting parameters such as object height and elevation using the Euler-region proposal network model.
Article
Computer Science, Information Systems
Wei Liang, Pengfei Xu, Ling Guo, Heng Bai, Yang Zhou, Feng Chen
Summary: With the rapid development of science and technology, 3D object detection is becoming increasingly important in the field of computer vision. This paper mainly focuses on deep learning-based 3D object detection methods, compares the experimental results of different methods, and discusses future research directions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Civil
Guangming Wang, Chensheng Peng, Yingying Gu, Jinpeng Zhang, Hesheng Wang
Summary: Multiple Object Tracking (MOT) is an important task in autonomous driving, but relying on a single sensor is not robust enough. Texture information from RGB cameras and 3D structure information from LiDAR have their own advantages in different situations. Therefore, feature fusion from multiple modalities is beneficial for learning discriminative features. However, achieving effective feature fusion is challenging due to the distinct information modalities.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Marine
Miguel Martin-Abadal, Manuel Pinar-Molina, Antoni Martorell-Torres, Gabriel Oliver-Codina, Yolanda Gonzalez-Cid
Summary: In recent years, the usage of Autonomous Underwater Vehicles (AUVs) has significantly reduced the workload and risks of interventions in underwater scenarios. This paper proposes the usage of a deep neural network to recognize pipes and valves in multiple underwater scenarios, achieving high recognition accuracy with PointNet neural network in underwater environments.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Biao Liu, Bihao Tian, Hengyang Wang, Junchao Qiao, Zhi Wang
Summary: This paper proposes two modules to improve the performance of 3D object detection. The first module reduces data loss by extracting more detailed initial voxel information and fully fusing context information. The second module extracts voxel features using a backbone neural network based on 3D sparse convolution and generates high-quality 3D proposal regions by a cross-connected region proposal network. Additionally, this paper extends the target generation strategy in the anchor-based algorithm, stabilizing the network performance for multiple objects.
NEURAL PROCESSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Maria Jose Gomez-Silva, Arturo de la Escalera, Jose Maria Armingol
Summary: This article proposes a hierarchical method for generating tracking global hypotheses to address the difficulties of multi-object tracking in complex scenarios. By dividing the data association process into different levels and properly combining various affinity metrics, the method can generate a global hypothesis that describes the assignment of identities and handle new individuals entering the scene.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Civil
Jorge Beltran, Carlos Guindel, Arturo de la Escalera, Fernando Garcia
Summary: Most sensor setups for onboard autonomous perception consist of LiDARs and vision systems, and an accurate calibration between the sensors is required. We propose a method to calibrate the extrinsic parameters of any pair of sensors, which can handle devices with different resolutions and poses, and outperforms existing methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Giulia Slavic, Abrham Shiferaw Alemaw, Lucio Marcenaro, David Martin Gomez, Carlo Regazzoni
Summary: This paper proposes a method for video-frame prediction and anomaly detection in autonomous vehicles using a Dynamic Bayesian Network framework and Deep Learning methods. The method leverages multi-modal information from different sensors and learns an appropriate latent space. The evaluation shows that this method can effectively detect abnormal behaviors in a closed environment.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Jesus Urdiales, David Martin, Jose Maria Armingol
Summary: This paper presents a tracking system based on the Kalman filter that uses a deep learning approach to the association problem. The proposed architecture consists of three neural networks for feature extraction, similarity calculation, and information extraction. The model follows the tracking-by-detection paradigm and has been trained to handle missed observations and reduce identity switches.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Review
Computer Science, Artificial Intelligence
Fredy Barrientos-Espillco, Esther Gasco, Clara I. Lopez-Gonzalez, Maria J. Gomez-Silva, Gonzalo Pajares
Summary: Cyanobacterial Harmful Algal Blooms (CyanoHABs) in lakes and reservoirs have increased due to environmental factors, making early detection crucial. Autonomous Surface Vehicles (ASVs) equipped with machine vision systems can be a useful alternative. This study proposes an image Semantic Segmentation approach based on Deep Learning with Convolutional Neural Networks (CNNs) to detect CyanoHABs from an ASV perspective.
APPLIED SOFT COMPUTING
(2023)
Article
Energy & Fuels
Monica Alonso, Hortensia Amaris, David Martin, Arturo de la Escalera
Summary: Connected autonomous electric vehicles (CAEVs) play a crucial role in the decarbonization of the transport sector and are integral to home energy management systems (HEMSs) alongside PV units and battery energy storage systems. However, uncertainties associated with CAEVs pose challenges to HEMSs, such as uncertain arrival and departure times, unknown battery states of charge, and variable PV production. A proposed HEMS based on proximal policy optimization (PPO) addresses these challenges through deep reinforcement learning. Simulation results demonstrate that the PPO algorithm outperforms conventional methods, achieving a significant daily energy cost reduction.
Article
Chemistry, Analytical
Gonzalo Aguilar Jimenez, Arturo de la Escalera Hueso, Maria J. Gomez-Silva
Summary: This article compares the performances of three reinforcement learning algorithms in UAV navigation and finds that DQN achieves the best target completion, while SARSA and A2C algorithms perform poorly. However, further analysis shows that fine-tuning A2C can surpass the performance of DQN under certain conditions.
Article
Chemistry, Analytical
Zhuoyao He, David Martin Gomez, Arturo de la Escalera Hueso, Pablo Flores Pena, Xingcai Lu, Jose Maria Armingol Moreno
Summary: Unmanned aerial vehicles (UAVs) face limitations in flight endurance due to the limited energy density of their batteries. This paper proposes a novel method, fast open circuit voltage (OCV), for obtaining battery OCVs, which offers advantages in simplicity, duration, and cost over traditional methods. Additionally, a batch mode is proposed for data sampling and battery-parameter identification, which effectively reduces noise compared to the single mode approach.
Article
Computer Science, Artificial Intelligence
Javier Dominguez-Martin, Maria J. Gomez-Silva, Arturo De La Escalera
Summary: This article explores the challenge of solving Person Re-Identification (Re-Id) through Deep Convolutional Neural Networks, particularly in the case of limited training data. Different neural architectures, trained using a Triplet Model, are evaluated on two challenging Single-Shot Re-Id datasets, PRID2011 and CUHK. The results suggest that Inception-ResNet and DenseNet are potentially useful models for Re-Id tasks.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Maria Vera, Maria Jose Gomez-Silva, Vicente Vera, Clara I. Lopez-Gonzalez, Ignacio Aliaga, Esther Gasco, Vicente Vera-Gonzalez, Maria Pedrera-Canal, Eva Besada-Portas, Gonzalo Pajares
Summary: An automatic image processing approach based on deep learning and image understanding techniques has been developed to assist dentists in determining bone loss around dental implants. The approach uses a deep learning object detector to roughly identify implants and crowns, and an image understanding process to optimize lines and detect significant points on screw edges. The performance evaluation shows satisfactory results in detecting bone loss.
JOURNAL OF DIGITAL IMAGING
(2023)
Proceedings Paper
Robotics
Alvaro Ramajo Ballester, Jacobo Gonzalez Cepeda, Jose Maria Armingol Moreno
Summary: The high precision of deep neural networks in visual perception tasks has the potential to extract important information from the environment, benefiting projects like autonomous vehicles and smart cities. This study aims to explore the latest methods and develop a system that efficiently solves two tasks: vehicle visual characterization and re-identification, and license plate segmentation and character recognition. A custom dataset is created to test and validate the system, bridging the gap between lab and real-world environments.
ROBOT2022: FIFTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
