Article
Agriculture, Multidisciplinary
Tianhai Wang, Bin Chen, Zhenqian Zhang, Han Li, Man Zhang
Summary: This paper reviews the applications of machine vision in agricultural robot navigation, introduces the advantages, disadvantages, and roles of different vision sensors and algorithms, discusses the challenges faced in this field, and looks forward to future research directions.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Biology
Mingqiang Li, Boquan Wang, Jianlin Yang, Jia Cao, Chenzhi You, Yizhe Sun, Jing Wang, Dawei Wu
Summary: This paper proposes a multistage adaptive control approach based on image contour data to improve the effectiveness of colonoscopy. By designing fast image preprocessing and contour extraction algorithms, and developing different processing algorithms based on extracted contour information, the endoscope navigation is successfully guided in an intestinal model. The experimental results demonstrate the effectiveness of this approach in both straight and curved sections.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Electrical & Electronic
Xiaobo Chen, Yukun Wang, Ying Sheng, Chengyi Yu, Xiao Yang, Juntong Xi
Summary: A machine vision-aided method is proposed to measure and track the ring groove positions as the brush alignment objective, improving the accuracy and efficiency of manual brush alignment assembly.
Article
Computer Science, Artificial Intelligence
Dongfang Li, Boliao Li, Shuo Kang, Huaiqu Feng, Sifang Long, Jun Wang
Summary: Crop row detection is crucial for visual navigation of agricultural machinery. In this study, a compact and efficient deep learning-based network named E2CropDet is proposed, which models each crop row as an independent object, enabling an end-to-end detection process with no post-processing. The network utilizes generic feature extractors and line-shaped proposals for detection, achieving remarkable results and a detection speed of 166 frames per second.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Agronomy
Jiayou Shi, Yuhao Bai, Zhihua Diao, Jun Zhou, Xingbo Yao, Baohua Zhang
Summary: Crop row detection is a challenging task in the field of agricultural robotics and autonomous vehicles due to the complex and dynamic agricultural environment. This paper provides a comprehensive review of methods and applications related to crop row detection for agricultural machinery navigation. It focuses on improving the perception and detection capabilities of sensors and systems used for crop row detection. The advantages, disadvantages, and applications of various crop row detection methods are discussed and summarized.
Article
Chemistry, Analytical
Eduardo Sanchez Morales, Julian Dauth, Bertold Huber, Andres Garcia Higuera, Michael Botsch
Summary: This research presents a methodology to reduce the dependency of Inertial Navigation Systems on SatNav, improving the accuracy of vehicle state estimation under adverse driving conditions. The proposed method includes machine learning for standstill recognition and LiDAR-based Positioning Method for indoor navigation, achieving accuracy closely resembling that of a system with Real-Time Kinematic correction data.
Article
Engineering, Electrical & Electronic
Yu Wang, Jian Li, Gangyi Wang, Wenbo Yu, Yan Ma
Summary: With the development of aerospace technology, optical navigation based on planetary image processing has become increasingly important. Traditional image processing approaches are limited to the capture phase and are susceptible to texture-rich features such as the celestial twilight line. In this paper, a new image processing algorithm is proposed to independently extract the planetary center location from an incomplete texture-rich celestial image with high precision.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Lili Zhu, Petros Spachos
Summary: Food quality and safety are crucial for human health and social stability. This study proposed a mobile visual system to grade bananas, achieving high accuracy rates in the grading process. The complex process of ensuring food quality involves all stages from cultivation to consumption.
INTERNET OF THINGS
(2021)
Article
Construction & Building Technology
Liang Zeng, Wenqiang Shu, Zhe Liu, Xinyi Zou, Shanshan Wang, Junyong Xia, Chao Xu, Dongdong Xiong, Zhao Yang
Summary: The paper studies the limitations of existing shield tail clearance measurement and monitoring technology and develops a high-precision intelligent monitoring system, including shield tail clearance calculation models, hardware components, ROI extraction method, and image processing algorithms. In experiments, the system achieved the goal of high-precision measuring and real-time monitoring of shield tail clearance.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Agricultural Engineering
Chufan Jiang, Ziping Liu, John T. Evans, Gregory M. Shaver, Logan J. Heusinger, Corwin M. Puryk
Summary: This paper describes a novel LiDAR-based benchmark system for evaluating the in-situ performance of combine harvester grain unloading on-the-go automation systems. The appropriate sensor for the benchmark system was chosen by comparing different LiDAR sensors based on their field-of-view and performance in dusty environments. The benchmark system provides accurate reference grain fill maps and runs simultaneously with the stereo-camera based system during unloading-on-the-go, providing benchmark data for quantitative analysis.
BIOSYSTEMS ENGINEERING
(2023)
Article
Environmental Sciences
William Yamada, Wei Zhao, Matthew Digman
Summary: An automatic method using monovision un-crewed aerial vehicle imagery was developed to obtain geographic coordinates of bales, with YOLOv3 algorithm identified as the best option in terms of accuracy and speed. Lowering image quality resulted in decreased performance.
Article
Agricultural Engineering
Yang Zhou, Yang Yang, Boli Zhang, Xing Wen, Xuan Yue, Liqing Chen
Summary: The study proposed an algorithm for detecting crop rows in maize fields based on adaptive multi-region of interest (multi-ROI), achieving a detection accuracy of 95.3% with an average computation time of 240.8 ms. The results indicate that the method performs well in field navigation and meets real-time and accuracy requirements.
INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING
(2021)
Review
Agriculture, Multidisciplinary
Yuhao Bai, Baohua Zhang, Naimin Xu, Jun Zhou, Jiayou Shi, Zhihua Diao
Summary: Autonomous navigation of agricultural robots in agricultural environments is challenging due to their complex and unstructured nature. Machine vision technology has been intensively studied and widely used in the field of agricultural navigation. This paper reviews the research advances in machine vision-based navigation and guidance for agricultural robots, discusses key visual navigation information processing technologies, and focuses on the application of vision-based navigation technology for agricultural robots. The challenges and future development prospects of machine vision in agricultural robot navigation are also discussed.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Electrical & Electronic
Tanmay Anand, Soumendu Sinha, Murari Mandal, Vinay Chamola, Fei Richard Yu
Summary: Aerial inspection of agricultural regions provides crucial information to safeguard efficient farming, while monitoring farmland anomalies is essential for increasing agricultural technology efficiency and developing AI-assisted farming models. The proposal of the deep learning framework AgriSegNet contributes to automated detection of farmland anomalies and enhancing precision farming techniques.
IEEE SENSORS JOURNAL
(2021)
Review
Agriculture, Multidisciplinary
Binbin Xie, Yucheng Jin, Muhammad Faheem, Wenjie Gao, Jizhan Liu, Houkang Jiang, Lianjiang Cai, Yuanxiang Li
Summary: Due to increasing demand for food, a global shortage of agricultural labor, and a reduction in the utilization of agricultural resources, the demand for agricultural autonomous navigation technology and equipment has become urgent. The complex and diverse agricultural scene requires different navigation modes and core technologies compared to self-driving cars. Therefore, summarizing the development characteristics and trends of autonomous navigation technology in various agricultural scenarios is of great value.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Computer Science, Interdisciplinary Applications
Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xavier Boucher, Camilo Murillo Coba, Damien Lamy
Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.
COMPUTERS IN INDUSTRY
(2024)