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Electronics
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Robotics Vision in Challenging Environment and Applications
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Robotics Vision in Challenging Environment and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 4987

Special Issue Editors

Dr. Chunhui Zhao

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
Interests: visual navigation of UAV; image processing; target tracking and recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Lyu

E-Mail
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
Interests: nnmanned systems; robitics navigation and control

Special Issue Information

Dear Colleagues,

Recently, mobile robots, unmanned systems, etc., have been widely applied in various environments and applications, imposing great challenges on autonomous functions of these systems, such as the positioning and navigation, mission-oriented perception, and reactive control.

Various sensors, such as the electrical optical camera, RGBD camera, and LiDAR, are applied to robot systems to demonstrate their effectiveness in environment sensing and autonomous task execution. However, the effectiveness of sensory techniques in challenging environments, such as highly dynamic environments, sensing degradation, and strong interference, still needs to be further investigated. This Special Issue aims to address issues involving sensing, perception techniques that favor unmanned systems, and robots to operate in challenging environments, which include aviation, aerospace, water surface, underwater, jungles, tunnels, etc.

We invite scholars in the field of unmanned system perception to present their research results, exchange scientific research experiences, and lead the research of robot perception technology for better development.

Dr. Chunhui Zhao
Dr. Yang Lyu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image processing
  • point cloud processing
  • information fusion
  • target tracking
  • target recognition
  • event camera
  • SLAM
  • reactive control
  • perception-aware control

Published Papers (4 papers)

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Research

14 pages, 4724 KiB  
Article
Research on the Detection Method of Coal Mine Roadway Bolt Mesh Based on Improved YOLOv7
by Siya Sun, Hongwei Ma, Keda Wang, Chuanwei Wang, Zhanhui Wang and Haining Yuan
Electronics 2023, 12(14), 3050; https://doi.org/10.3390/electronics12143050 - 12 Jul 2023
Cited by 2 | Viewed by 910
Abstract
Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and [...] Read more.
Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s. Full article
(This article belongs to the Special Issue Robotics Vision in Challenging Environment and Applications)
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16 pages, 4687 KiB  
Article
Wind Turbine Gearbox Gear Surface Defect Detection Based on Multiscale Feature Reconstruction
by Rui Gao, Jingfei Cao, Xiangang Cao, Jingyi Du, Hang Xue and Daming Liang
Electronics 2023, 12(14), 3039; https://doi.org/10.3390/electronics12143039 - 11 Jul 2023
Cited by 1 | Viewed by 788
Abstract
The fast and accurate detection of wind turbine gearbox surface defects is crucial for wind turbine maintenance and power security. However, owing to the uneven distribution of gear surface defects and the interference of complex backgrounds, there are limitations to gear-surface defect detection; [...] Read more.
The fast and accurate detection of wind turbine gearbox surface defects is crucial for wind turbine maintenance and power security. However, owing to the uneven distribution of gear surface defects and the interference of complex backgrounds, there are limitations to gear-surface defect detection; therefore, this paper proposes a multiscale feature reconstruction-based detection method for wind turbine gearbox surface defects. First, the Swin Transformer was used as a backbone network based on the PSPNet network to obtain global and local features through multiscale feature reconstruction. Second, a Feature Similarity Module was used to filter important feature sub-blocks, which increased the inter-class differences and reduced the intra-class differences to enhance the discriminative ability of the model for similar features. Finally, the fusion of contextual information using the pyramid pooling module enhanced the extraction of gear surface defect features at different scales. The experimental results indicated that the improved algorithm outperformed the original PSPNet algorithm by 1.21% and 3.88% for the mean intersection over union and mean pixel accuracy, respectively, and significantly outperformed semantic segmentation networks such as U-Net and DeepLabv3+. Full article
(This article belongs to the Special Issue Robotics Vision in Challenging Environment and Applications)
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19 pages, 7483 KiB  
Article
MGFCTFuse: A Novel Fusion Approach for Infrared and Visible Images
by Shuai Hao, Jiahao Li, Xu Ma, Siya Sun, Zhuo Tian and Le Cao
Electronics 2023, 12(12), 2740; https://doi.org/10.3390/electronics12122740 - 20 Jun 2023
Cited by 1 | Viewed by 879
Abstract
Traditional deep-learning-based fusion algorithms usually take the original image as input to extract features, which easily leads to a lack of rich details and background information in the fusion results. To address this issue, we propose a fusion algorithm, based on mutually guided [...] Read more.
Traditional deep-learning-based fusion algorithms usually take the original image as input to extract features, which easily leads to a lack of rich details and background information in the fusion results. To address this issue, we propose a fusion algorithm, based on mutually guided image filtering and cross-transmission, termed MGFCTFuse. First, an image decomposition method based on mutually guided image filtering is designed, one which decomposes the original image into a base layer and a detail layer. Second, in order to preserve as much background and detail as possible during feature extraction, the base layer is concatenated with the corresponding original image to extract deeper features. Moreover, in order to enhance the texture details in the fusion results, the information in the visible and infrared detail layers is fused, and an enhancement module is constructed to enhance the texture detail contrast. Finally, in order to enhance the communication between different features, a decoding network based on cross-transmission is designed within feature reconstruction, which further improves the quality of image fusion. In order to verify the advantages of the proposed algorithm, experiments are conducted on the TNO, MSRS, and RoadScene image fusion datasets, and the results demonstrate that the algorithm outperforms nine comparative algorithms in both subjective and objective aspects. Full article
(This article belongs to the Special Issue Robotics Vision in Challenging Environment and Applications)
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17 pages, 2391 KiB  
Article
Dynamic Target Tracking of Small UAVs in Unstructured Environment
by Haiqing Li, Mengbo Yang, Yanbo Li, Liming Dai and Chunhui Zhao
Electronics 2023, 12(5), 1078; https://doi.org/10.3390/electronics12051078 - 21 Feb 2023
Cited by 1 | Viewed by 1329
Abstract
In this paper, an adaptive multi-rotor UAV system of dynamic target tracking and path planning is proposed for the problems of occlusion, lighting change, and similar target interference in an unstructured environment. A DTE-tracker module is designed, consisting of a detector, tracker, and [...] Read more.
In this paper, an adaptive multi-rotor UAV system of dynamic target tracking and path planning is proposed for the problems of occlusion, lighting change, and similar target interference in an unstructured environment. A DTE-tracker module is designed, consisting of a detector, tracker, and examiner, and proposes a dynamic target capture mechanism to improve the robustness and continuity of target tracking in complex environments. A DWA local path planning algorithm based on a dynamic target tracking task is proposed to control the yaw of the UAV to accurately locate specific targets in the center of the image, and finally achieve the purpose of stable tracking. A UAV platform was built equipped with an onboard computer, laser sensor, and visual sensor, and a series of target tracking and path planning experiments were carried out to verify the effectiveness of the method and test the performance of the algorithm in a complex jungle environment. Full article
(This article belongs to the Special Issue Robotics Vision in Challenging Environment and Applications)
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