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Computer Vision Recognition and Communication Sensing System

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 10192

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Special Issue Editors

Dr. Weizhong Qian

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Guest Editor

School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: Internet of Things; signal processing and data fusion in sensor systems; logistics big data application; computer vision perception; software security; OOP (object-oriented programming); image and digital signal processing

Dr. Weimin Shi

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Guest Editor

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: wireless communication; power amplifier design; power electronic circuits design of new energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Yue Xiu

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Guest Editor

Communication and Information System in Signal Processing and Information Theory, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: signal processing; MmWave communication; communication system; physical communication; intelligent communication; information theory; RIS; UAV

Special Issue Information

Dear Colleagues,

Visual recognition and communication perception are two fundamental components of cognitive science and human–computer interactions that play crucial roles in interpreting and understanding the world around us. Visual recognition refers to the ability to identify and process visual stimuli, encompassing everything from recognizing faces and objects to understanding complex scenes and spatial relationships. This capability is not only pivotal in everyday tasks but also forms the backbone of numerous applications in fields such as security, autonomous driving, and augmented reality. On the other hand, communication perception extends beyond the visual to include the interpretation of verbal and non-verbal cues in a communicative context. It involves the decoding of language, intonation, gestures, and facial expressions, enabling individuals to derive meaning and respond appropriately in social interactions. This perceptual ability is fundamental for effective communication and is integral to building relationships, facilitating collaboration, and navigating social complexities. Both visual recognition and communication perception are deeply intertwined with artificial intelligence research, particularly in enhancing machine learning models that aim to replicate human-like understanding and responsiveness. By advancing our knowledge in these areas, we can develop more intuitive and effective systems that better mimic human perceptual and communicative capacities, leading to improvements in technology-mediated interactions and interfaces.

This Special Issue aims to present the advantages and research trends of visual detection and wireless communication technology in multi-dimensional perception of unknown environments, especially based on AI algorithms. Topics of interest for publications include, but are not limited to, the following:

Visual recognition perception and tracking in industrial scenes;
Integrated sensing and communication;
Infrared visual detection and perception;
Visual integration navigation and environmental awareness;
3D visual pose estimation;
Applications of deep learning in the field of vision.

Dr. Weizhong Qian
Dr. Weimin Shi
Dr. Yue Xiu
Guest Editors

Manuscript Submission Information

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Keywords

visual recognition perception
waveform design
visual positioning navigation
deep learning
algorithm design
integrated sensing and communication

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Published Papers (9 papers)

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Research

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19 pages, 7040 KiB

Open AccessArticle

Research on an Online Intelligent Monitoring System for Resistance Spot Welding Based on Wireless Communication

by Shuwan Cui, Xuan Zhou, Baoyan Zhang, Leigang Han, Bin Xue and Feiyang Liu

Sensors 2025, 25(9), 2658; https://doi.org/10.3390/s25092658 - 23 Apr 2025

Viewed by 212

Abstract

Resistance spot welding (RSW) faces critical monitoring challenges in industrial applications due to nonlinear coupling characteristics and production line disturbances. This study developed a Zigbee-enabled real-time monitoring system to address the precision limitations of conventional methods in tracking RSW parameters. Using DP780/DP590 dual-phase steel specimens with thickness variations, we implemented a dedicated data acquisition system capturing welding current, voltage, and barometric pressure dynamics. The experimental results demonstrated measurement accuracies within ±0.49% for current, ±0.25% for voltage, and 3.72% average relative error for barometric pressure with stable operational deviations (0.017–0.024 MPa). Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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29 pages, 6807 KiB

Open AccessArticle

IoT-Based Airport Noise Perception and Monitoring: Multi-Source Data Fusion, Spatial Distribution Modeling, and Analysis

by Jie Liu, Shiman Sun, Ke Tang, Xinyu Fan, Jihong Lv, Yinxiang Fu, Xinpu Feng and Liang Zeng

Sensors 2025, 25(8), 2347; https://doi.org/10.3390/s25082347 - 8 Apr 2025

Viewed by 237

Abstract

With the acceleration of global urbanization, airport noise pollution has emerged as a significant environmental concern that demands attention. Traditional airport noise monitoring systems are fraught with limitations, including restricted spatial coverage, inadequate real-time data acquisition capabilities, poor data correlation, and suboptimal cost-effectiveness. To address these challenges, this paper proposes an innovative airport noise perception and monitoring approach leveraging Internet of Things (IoT) technology. This method integrates multiple data streams, encompassing noise, meteorological, and ADS–B data, to achieve precise noise event tracing and deep multi-source data fusion. Furthermore, this study employs Kriging interpolation and Inverse Distance Weighting (IDW) techniques to perform spatial interpolation on data from sparse monitoring sites, thereby constructing a spatial distribution model of airport noise. The results of the practical application demonstrate that the proposed airport noise monitoring method can accurately reflect the spatiotemporal distribution patterns of airport noise and effectively correlate noise events, thereby providing robust data support for the development of airport noise control policies. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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31 pages, 11795 KiB

Open AccessArticle

DT-YOLO: An Improved Object Detection Algorithm for Key Components of Aircraft and Staff in Airport Scenes Based on YOLOv5

by Zhige He, Yuanqing He and Yang Lv

Sensors 2025, 25(6), 1705; https://doi.org/10.3390/s25061705 - 10 Mar 2025

Viewed by 623

Abstract

With the rapid development and increasing demands of civil aviation, the accurate detection of key aircraft components and staff on airport aprons is of great significance for ensuring the safety of flights and improving the operational efficiency of airports. However, the existing detection models for airport aprons are relatively scarce, and their accuracy is insufficient. Based on YOLOv5, we propose an improved object detection algorithm, called DT-YOLO, to address these issues. We first built a dataset called AAD-dataset for airport apron scenes by randomly sampling and capturing surveillance videos taken from the real world to support our research. We then introduced a novel module named D-CTR in the backbone, which integrates the global feature extraction capability of Transformers with the limited receptive field of convolutional neural networks (CNNs) to enhance the feature representation ability and overall performance. A dropout layer was introduced to reduce redundant and noisy features, prevent overfitting, and improve the model’s generalization ability. In addition, we utilized deformable convolutions in CNNs to extract features from multi-scale and deformed objects, further enhancing the model’s adaptability and detection accuracy. In terms of loss function design, we modified GIoULoss to address its discontinuities and instability in certain scenes, which effectively mitigated gradient explosion and improved the stability of the model. Finally, experiments were conducted on the self-built AAD-dataset. The results demonstrated that DT-YOLO significantly improved the mean average precision (mAP). Specifically, the mAP increased by 2.6 on the AAD-dataset; moreover, other metrics also showed a certain degree of improvement, including detection speed, AP50, AP75, and so on, which comprehensively proves that DT-YOLO can be applied for real-time object detection in airport aprons, ensuring the safe operation of aircraft and efficient management of airports. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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16 pages, 790 KiB

Open AccessArticle

Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory

by Qilong Miao, Xiaofeng Shen, Chenfei Xie, Yong Gao and Lu Chen

Sensors 2025, 25(2), 465; https://doi.org/10.3390/s25020465 - 15 Jan 2025

Cited by 1 | Viewed by 752

Abstract

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system’s target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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25 pages, 2170 KiB

Open AccessArticle

Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems

by Chenfei Xie, Yue Xiu, Songjie Yang, Qilong Miao, Lu Chen, Yong Gao and Zhongpei Zhang

Sensors 2025, 25(2), 388; https://doi.org/10.3390/s25020388 - 10 Jan 2025

Viewed by 1073

Abstract

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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19 pages, 5550 KiB

Open AccessArticle

Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration

by Dezhou Yuan, Yingxue Zhong, Xinping Zhu, Ying Chen, Yue Jin, Xinze Du, Ke Tang and Tianyu Huang

Sensors 2025, 25(1), 71; https://doi.org/10.3390/s25010071 - 26 Dec 2024

Viewed by 689

Abstract

To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin–destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft–vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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27 pages, 9279 KiB

Open AccessArticle

An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism

by Lu Chen, Yuhao Yang, Tianci Wu, Chiang Liu, Yang Li, Jie Tan, Weizhong Qian, Liang Yang, Yue Xiu and Gun Li

Sensors 2024, 24(20), 6733; https://doi.org/10.3390/s24206733 - 19 Oct 2024

Cited by 1 | Viewed by 1319

Abstract

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model’s superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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22 pages, 15279 KiB

Open AccessArticle

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer

by Yuhai Li, Youchen Fan, Shunhu Hou, Yufei Niu, You Fu and Hanzhe Li

Sensors 2024, 24(14), 4562; https://doi.org/10.3390/s24144562 - 14 Jul 2024

Viewed by 1606

Abstract

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods’ intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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Review

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35 pages, 848 KiB

Open AccessReview

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation

by Zhuang Wang, Peiyuan Jiang, Zixuan Wang, Boyuan Han, Haijun Liang, Yi Ai and Weijun Pan

Sensors 2024, 24(14), 4715; https://doi.org/10.3390/s24144715 - 20 Jul 2024

Viewed by 3156

Abstract

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology’s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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