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Deep Learning for Perception and Recognition: Method and Applications

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

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 10600

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Dr. Gaochang Wu

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State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China
Interests: multimodal perception and recognition; light field processing; and computer vision in industrial scenarios

Dr. Zizhu Fan

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Guest Editor

College of Computer Science and Technology, Shanghai Electric Power University, Shanghai, China
Interests: pattern recognition and machine learning; interpretable artificial intelligence; computer vision; smart grid

Dr. Dong Pan

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Guest Editor

School of Automation, Central South University, Changsha 410083, China
Interests: infrared thermography; temperature measurement; deep learning; vision-based measurement; object detection; information fusion
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Special Issue Information

Dear Colleagues,

The rapid advancement of deep learning technology has brought about transformative breakthroughs in perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligent transportation, smart cities, healthcare, and robotics.

Deep learning significantly enhances the accuracy and robustness of perception and recognition systems through hierarchical feature extraction in multilayer neural networks, achieving remarkable results in areas such as soft sensing, image classification, natural language processing, and object detection. By training on large volumes of labeled data, deep learning algorithms are able to automatically learn complex feature representations and efficiently recognize objects during the perception process.

As application scenarios grow more complex and data become increasingly diverse, deep learning models continue to face significant challenges in solving real-world perception and recognition problems. These challenges include ensuring model generalization when dealing with noisy, imbalanced, or limited data; enhancing performance through self-supervised, few-shot, or transfer learning in cases of insufficient labeled data; integrating information across different scales, dimensions, and modalities; and developing explainable perception and recognition systems for high-risk applications.

This Special Issue seeks to highlight advanced research in deep learning for perception and recognition. Submitted papers should clearly present novel contributions, whether in general methodologies or innovative applications, addressing any of the following topics:

Image and video processing;
Visual intelligent perception;
Multimodal information fusion and learning;
Perception and recognition with applications;
Pattern recognition and analysis;
Knowledge and learning system with applications;
Explainable machine learning.

Dr. Gaochang Wu
Dr. Zizhu Fan
Dr. Dong Pan
Guest Editors

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Keywords

image processing
visual perception
multimodal learning
multimodal fusion
pattern analysis
knowledge system
explainable machine learning

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Published Papers (12 papers)

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Research

15 pages, 4638 KiB

Open AccessArticle

Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks

by Zvi Stein, Adir Hazan and Adrian Stern

Sensors 2025, 25(7), 2301; https://doi.org/10.3390/s25072301 - 4 Apr 2025

Viewed by 288

Abstract

Despite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adversarial attack via dazzling a CMOS camera. This attack involves using a designed light pulse sequence spatially transformed within the acquired image due to the camera’s shutter mechanism. We provide a detailed analysis of the photopic conditions required to keep the attacking light source invisible to human observers while effectively disrupting the image, thereby deceiving the DNN. The results indicate that the light source duty cycle controls the tradeoff between the attack’s success rate and the degree of concealment needed. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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21 pages, 2969 KiB

Open AccessArticle

HGF-MiLaG: Hierarchical Graph Fusion for Emotion Recognition in Conversation with Mid-Late Gender-Aware Strategy

by Yihan Wang, Rongrong Hao, Ziheng Li, Xinhe Kuang, Jiacheng Dong, Qi Zhang, Fengkui Qian and Changzeng Fu

Sensors 2025, 25(4), 1182; https://doi.org/10.3390/s25041182 - 14 Feb 2025

Viewed by 620

Abstract

Emotion recognition in conversation (ERC) is an important research direction in the field of human-computer interaction (HCI), which recognizes emotions by analyzing utterance signals to enhance user experience and plays an important role in several domains. However, existing research on ERC mainly focuses on constructing graph networks by directly modeling interactions on multimodal fused features, which cannot adequately capture the complex dialog dependency based on time, speaker, modalities, etc. In addition, existing multi-task learning frameworks for ERC do not systematically investigate how and where gender information is injected into the model to optimize ERC performance. To address the above problems, this paper proposes a Hierarchical Graph Fusion for ERC with Mid-Late Gender-aware Strategy (HGF-MiLaG). HGF-MiLaG uses hierarchical fusion graph to adequately capture intra-modal and inter-modal speaker dependency and temporal dependency. In addition, HGF-MiLaG explores the effect of the location of gender information injections on ERC performance, and ultimately employs a Mid-Late multilevel gender-aware strategy in order to allow the hierarchical graph network to determine the proportion of emotion and gender information in the classifier. Empirical results on two public multimodal datasets(i.e.,IEMOCAP and MELD), demonstrate that HGF-MiLaG outperforms existing methods. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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14 pages, 12776 KiB

Open AccessArticle

A Projective-Geometry-Aware Network for 3D Vertebra Localization in Calibrated Biplanar X-Ray Images

by Kangqing Ye, Wenyuan Sun, Rong Tao and Guoyan Zheng

Sensors 2025, 25(4), 1123; https://doi.org/10.3390/s25041123 - 13 Feb 2025

Viewed by 654

Abstract

Current Deep Learning (DL)-based methods for vertebra localization in biplanar X-ray images mainly focus on two-dimensional (2D) information and neglect the projective geometry, limiting the accuracy of 3D navigation in X-ray-guided spine surgery. A 3D vertebra localization method from calibrated biplanar X-ray images is highly desired to address the problem. In this study, a projective-geometry-aware network for localizing 3D vertebrae in calibrated biplanar X-ray images, referred to as ProVLNet, is proposed. The network design of ProVLNet features three components: a Siamese 2D feature extractor to extract local appearance features from the biplanar X-ray images, a spatial alignment fusion module to incorporate the projective geometry in fusing the extracted 2D features in 3D space, and a 3D landmark regression module to regress the 3D coordinates of the vertebrae from the 3D fused features. Evaluated on two typical and challenging datasets acquired from the lumbar and the thoracic spine, ProVLNet achieved an identification rate of 99.53% and 98.98% and a point-to-point error of 0.64 mm and 1.38 mm, demonstrating superior performance of our proposed approach over the state-of-the-art (SOTA) methods. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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18 pages, 12390 KiB

Open AccessArticle

DeiT and Image Deep Learning-Driven Correction of Particle Size Effect: A Novel Approach to Improving NIRS-XRF Coal Quality Analysis Accuracy

by Jiaxin Yin, Ruonan Liu, Wangbao Yin, Suotang Jia and Lei Zhang

Sensors 2025, 25(3), 928; https://doi.org/10.3390/s25030928 - 4 Feb 2025

Viewed by 722

Abstract

Coal, as a vital global energy resource, directly impacts the efficiency of power generation and environmental protection. Thus, rapid and accurate coal quality analysis is essential to promote its clean and efficient utilization. However, combined near-infrared spectroscopy and X-ray fluorescence (NIRS-XRF) spectroscopy often suffer from the particle size effect of coal samples, resulting in unstable and inaccurate analytical outcomes. This study introduces a novel correction method combining the Segment Anything Model (SAM) for precise particle segmentation and Data-Efficient Image Transformers (DeiTs) to analyze the relationship between particle size and ash measurement errors. Microscopic images of coal samples are processed with SAM to generate binary mask images reflecting particle size characteristics. These masks are analyzed using the DeiT model with transfer learning, building an effective correction model. Experiments show a 22% reduction in standard deviation (SD) and root mean square error (RMSE), significantly enhancing ash prediction accuracy and consistency. This approach integrates cutting-edge image processing and deep learning, effectively reducing submillimeter particle size effects, improving model adaptability, and enhancing measurement reliability. It also holds potential for broader applications in analyzing complex samples, advancing automation and efficiency in online analytical systems, and driving innovation across industries. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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28 pages, 16917 KiB

Open AccessArticle

A Framework of State Estimation on Laminar Grinding Based on the CT Image–Force Model

by Jihao Liu, Guoyan Zheng and Weixin Yan

Sensors 2025, 25(1), 238; https://doi.org/10.3390/s25010238 - 3 Jan 2025

Viewed by 752

Abstract

It is a great challenge for a safe surgery to localize the cutting tip during laminar grinding. To address this problem, we develop a framework of state estimation based on the CT image–force model. For the proposed framework, the pre-operative CT image and intra-operative milling force signal work as source inputs. In the framework, a bone milling force prediction model is built, and the surgical planned paths can be transformed into the prediction sequences of milling force. The intra-operative milling force signal is segmented by the tumbling window algorithm. Then, the similarity between the prediction sequences and the segmented milling signal is derived by the dynamic time warping (DTW) algorithm. The derived similarity indicates the position of the cutting tip. Finally, to overcome influences of some factors, we used the random sample consensus (RANSAC). The code of the functional simulations has be opened. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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19 pages, 2113 KiB

Open AccessArticle

3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness

by Wei Zhuang, Yunhong Zhang, Yuan Wang and Kaiyang He

Sensors 2024, 24(23), 7856; https://doi.org/10.3390/s24237856 - 9 Dec 2024

Viewed by 874

Abstract

Evaluating students’ learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students’ learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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15 pages, 884 KiB

Open AccessArticle

Node Classification Method Based on Hierarchical Hypergraph Neural Network

by Feng Xu, Wanyue Xiong, Zizhu Fan and Licheng Sun

Sensors 2024, 24(23), 7655; https://doi.org/10.3390/s24237655 - 29 Nov 2024

Viewed by 959

Abstract

Hypergraph neural networks have gained widespread attention due to their effectiveness in handling graph-structured data with complex relationships and multi-dimensional interactions. However, existing hypergraph neural network models mainly rely on planar message-passing mechanisms, which have limitations: (i) low efficiency in encoding long-distance information; (ii) underutilization of high-order neighborhood features, aggregating information only on the edges of the original graph. This paper proposes an innovative hierarchical hypergraph neural network (HCHG) to address these issues. The HCHG combines the high-order relationship-capturing capability of hypergraphs, uses the Louvain community detection algorithm to identify community structures within the network, and constructs hypergraphs layer by layer. In the bottom-level hypergraph, the model establishes high-order relationships through direct neighbor nodes, while in the top-level hypergraph, it captures global relationships between aggregated communities. Through three hierarchical message-passing mechanisms, the HCHG effectively integrates local and global information, enhancing the multi-resolution representation ability of node representations and significantly improving performance in node classification tasks. In addition, the model performs excellently in handling 3D multi-view datasets. Such datasets can be created by capturing 3D shapes and geometric features through sensors or by manual modeling, providing extensive application scenarios for analyzing three-dimensional shapes and complex geometric structures. Theoretical analysis and experimental results show that the HCHG outperforms traditional hypergraph neural networks in complex networks. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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15 pages, 458 KiB

Open AccessArticle

Facial Anti-Spoofing Using “Clue Maps”

by Liang Yu Gong, Xue Jun Li and Peter Han Joo Chong

Sensors 2024, 24(23), 7635; https://doi.org/10.3390/s24237635 - 29 Nov 2024

Viewed by 714

Abstract

Spoofing attacks (or Presentation Attacks) are easily accessible to facial recognition systems, making the online financial system vulnerable. Thus, it is urgent to develop an anti-spoofing solution with superior generalization ability due to the high demand for spoofing attack detection. Although multi-modality methods such as combining depth images with RGB images and feature fusion methods could currently perform well with certain datasets, the cost of obtaining the depth information and physiological signals, especially that of the biological signal is relatively high. This paper proposes a representation learning method of an Auto-Encoder structure based on Swin Transformer and ResNet, then applies cross-entropy loss, semi-hard triplet loss, and Smooth L1 pixel-wise loss to supervise the model training. The architecture contains three parts, namely an Encoder, a Decoder, and an auxiliary classifier. The Encoder part could effectively extract the features with patches’ correlations and the Decoder aims to generate universal “Clue Maps” for further contrastive learning. Finally, the auxiliary classifier is adopted to assist the model in making the decision, which regards this result as one preliminary result. In addition, extensive experiments evaluated Attack Presentation Classification Error Rate (APCER), Bonafide Presentation Classification Error Rate (BPCER) and Average Classification Error Rate (ACER) performances on the popular spoofing databases (CelebA, OULU, and CASIA-MFSD) to compare with several existing anti-spoofing models, and our approach could outperform existing models which reach 1.2% and 1.6% ACER on intra-dataset experiment. In addition, the inter-dataset on CASIA-MFSD (training set) and Replay-attack (Testing set) reaches a new state-of-the-art performance with 23.8% Half Total Error Rate (HTER). Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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17 pages, 3450 KiB

Open AccessArticle

Coal and Gangue Detection Networks with Compact and High-Performance Design

by Xiangyu Cao, Huajie Liu, Yang Liu, Junheng Li and Ke Xu

Sensors 2024, 24(22), 7318; https://doi.org/10.3390/s24227318 - 16 Nov 2024

Viewed by 798

Abstract

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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37 pages, 15011 KiB

Open AccessArticle

Steering-Angle Prediction and Controller Design Based on Improved YOLOv5 for Steering-by-Wire System

by Cunliang Ye, Yunlong Wang, Yongfu Wang and Yan Liu

Sensors 2024, 24(21), 7035; https://doi.org/10.3390/s24217035 - 31 Oct 2024

Cited by 1 | Viewed by 1388

Abstract

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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24 pages, 5816 KiB

Open AccessArticle

Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments

by Mangali Sravanthi, Sravan Kumar Gunturi, Mangali Chinna Chinnaiah, Siew-Kei Lam, G. Divya Vani, Mudasar Basha, Narambhatla Janardhan, Dodde Hari Krishna and Sanjay Dubey

Sensors 2024, 24(21), 6986; https://doi.org/10.3390/s24216986 - 30 Oct 2024

Viewed by 1182

Abstract

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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15 pages, 2372 KiB

Open AccessArticle

PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation

by Peng Zhou, Hong Fang and Gaochang Wu

Sensors 2024, 24(21), 6908; https://doi.org/10.3390/s24216908 - 28 Oct 2024

Cited by 1 | Viewed by 1054

Abstract

Defects in photovoltaic (PV) panels can significantly reduce the power generation efficiency of the system and may cause localized overheating due to uneven current distribution. Therefore, adopting precise pixel-level defect detection, i.e., defect segmentation, technology is essential to ensuring stable operation. However, for effective defect segmentation, the feature extractor must adaptively determine the appropriate scale or receptive field for accurate defect localization, while the decoder must seamlessly fuse coarse-level semantics with fine-grained features to enhance high-level representations. In this paper, we propose a Progressive Deformable Transformer (PDeT) for defect segmentation in PV cells. This approach effectively learns spatial sampling offsets and refines features progressively through coarse-level semantic attention. Specifically, the network adaptively captures spatial offset positions and computes self-attention, expanding the model’s receptive field and enabling feature extraction across objects of various shapes. Furthermore, we introduce a semantic aggregation module to refine semantic information, converting the fused feature map into a scale space and balancing contextual information. Extensive experiments demonstrate the effectiveness of our method, achieving an mIoU of 88.41% on our solar cell dataset, outperforming other methods. Additionally, to validate the PDeT’s applicability across different domains, we trained and tested it on the MVTec-AD dataset. The experimental results demonstrate that the PDeT exhibits excellent recognition performance in various other scenarios as well. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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