Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Yao, Lina; Dong, Hui; Yang, Zhichun; Li, Haibin; Qian, Mengbo

doi:10.25165/j.ijabe.20191204.4821

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The integration of machine vision systems in road navigation on specific road models is an additional application. According to our investigation, the processes of interest in this field are related to detection [111] and inventory [118].…”

Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 99%

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tzampazaki,

Zografos,

Vrochidou

et al. 2024

Applied Sciences

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The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity.

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Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 99%

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tzampazaki,

Zografos,

Vrochidou

et al. 2024

Applied Sciences

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“…Unlike structured urban roads, field roads lack clear boundaries, and there are various obstacles such as tree shadows and weeds on both sides of the road, which make identifying road sections more challenging. Lidar has antiinterference characteristics and is not affected by lighting conditions [3,4] , making it a popular choice for obstacle detection [5] , tracking [6] , and road recognition [7] in unmanned driving. Road recognition based on Lidar can be divided into traditional and deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Recognition of field roads based on improved U-Net++ Network

Yang¹,

Li²,

Chang³

et al. 2023

International Journal of Agricultural and Biological Engineering

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Unmanned driving of agricultural machinery has garnered significant attention in recent years, especially with the development of precision farming and sensor technologies. To achieve high performance and low cost, perception tasks are of great importance. In this study, a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural machinery. The approach of this study utilized point clouds, with low-resolution Lidar point clouds as inputs, generating high-resolution point clouds and Bird's Eye View (BEV) images that were encoded with several basic statistics. Using a BEV representation, road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++ neural network. Three enhancements were proposed for U-Net++: 1) replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block (ACBlock); 2) adding a multi-branch Asymmetric Dilated Convolutional Block (MADC) in the highest semantic information layer; 3) adding an Attention Gate (AG) model to the long-skip-connection in the decoding stage. The results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds, which was 7.35 percentage points higher than U-Net++. Furthermore, the average processing time of the model was about 70 ms, meeting the time requirements of unmanned driving in agricultural machinery. The proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.

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“…Experiments can only be conducted at specific times of the year. Moreover, research in autonomous agricultural vehicles that are required to enable more efficient autonomy becomes increasingly demanding due to the growing simulation technology, and field trials are sometimes accompanied by safety hazards [6][7][8] . These problems have become restraints for the fast development of agricultural machinery technology.…”

Section: Introductionmentioning

confidence: 99%

Digital twins in smart farming: An autoware-based simulator for autonomous agricultural vehicles

Zhao,

Wang,

Wen

et al. 2023

International Journal of Agricultural and Biological Engineering

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Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of technologies. The autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field works. Autonomous vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research hotspot. However, the development time and resources required in experiments have limited the research in this area. Simulation tools in unmanned farming that are required to enable more efficient, reliable, and safe autonomy are increasingly demanding. Inspired by the recent development of an open-source virtual simulation platform, this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital twins. Oblique photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality. A communication format suitable for agricultural machines was developed for data input and output, along with an inter-node communication methodology. The conversion, publishing, and maintenance of multiple coordinate systems were completed based on ROS (Robot Operating System). Coverage path planning was performed using hybrid curves based on Bézier curves, and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers.

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Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Cited by 5 publications

References 12 publications

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Recognition of field roads based on improved U-Net++ Network

Digital twins in smart farming: An autoware-based simulator for autonomous agricultural vehicles

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