Perceptual Field Research Articles

Precision agriculture with YOLO-Leaf: advanced methods for detecting apple leaf diseases.

The detection of apple leaf diseases plays a crucial role in ensuring crop health and yield. However, due to variations in lighting and shadow, as well as the complex relationships between perceptual fields and target scales, current detection methods face significant challenges. To address these issues, we propose a new model called YOLO-Leaf. Specifically, YOLO-Leaf utilizes Dynamic Snake Convolution (DSConv) for robust feature extraction, employs BiFormer to enhance the attention mechanism, and introduces IF-CIoU to improve bounding box regression for increased detection accuracy and generalization ability. Experimental results on the FGVC7 and FGVC8 datasets show that YOLO-Leaf significantly outperforms existing models in terms of detection accuracy, achieving mAP50 scores of 93.88% and 95.69%, respectively. This advancement not only validates the effectiveness of our approach but also highlights its practical application potential in agricultural disease detection.

Speckle-free holography with a diffraction-aware global perceptual model

Computer-generated holography (CGH) based on neural networks has been actively investigated in recent years, and convolutional neural networks (CNNs) are frequently adopted. A convolutional kernel captures local dependencies between neighboring pixels. However, in CGH, each pixel on the hologram influences all the image pixels on the observation plane, thus requiring a network capable of learning long-distance dependencies. To tackle this problem, we propose a CGH model called Holomer. Its single-layer perceptual field is 43 times larger than that of a widely used 3×3 convolutional kernel, thanks to the embedding-based feature dimensionality reduction and multi-head sliding-window self-attention mechanisms. In addition, we propose a metric to measure the networks’ learning ability of the inverse diffraction process. In the simulation, our method demonstrated noteworthy performance on the DIV2K dataset at a resolution of 1920×1024, achieving a PSNR and an SSIM of 35.59 dB and 0.93, respectively. The optical experiments reveal that our results have excellent image details and no observable background speckle noise. This work paves the path of high-quality hologram generation.

Estilos Cognitivos en Niños con Trastorno del Espectro Autista y con Trastorno por Déficit de Atención con Hiperactividad

The aim was to determine the performance in the cognitive styles test of children diagnosed with Autism Spectrum Disorder (ASD) and Attention Deficit Disorder with Hyperactivity (ADHD) and to establish the cognitive profile of these two populations. The sample consisted of 50 boys and girls belonging to the Institute for the Integral Development of Children with Autism (DINA) and attending the Rafael Henao Toro Children's Hospital in Manizales, Caldas; the Masked Figures Test for Children (CEFT) was applied to measure the cognitive styles construct. It was determined that there were statistically significant differences between groups in the cognitive styles test score, finding that children with ASD present a perceptual field independent cognitive profile; on the contrary, it is evident that children with ADHD have a predominantly perceptual field dependent profile.

Acoustic Depth

Summary Acoustic depth is an elusive indicator that touches the world of emergencies, or perceptual focus, in a peculiar way. The realm in which these forms of recognition operate indeed links media theory to perception theory and aesthetic reflection. Expressions such as acoustic scene, sound depth or sharpness, or acoustic image when discussing the receptive forms of various microphone models, are just a few cases where imaginative synthesis translates the sense of phenomena arising from the resistance of sensory properties to the structures of linguistic formalization. The language of media is full of these references, which seem to refer to a common sense that emerges in the differentiation of perceptual channels, but is rarely made explicit in a clear manner. To understand acoustic depht’s nature, we can use the concept of emergence: every time we hear a sound, that acoustic process is always surrounded by a myriad of small sounds that interact with it. The notion of emergence can be applied to anything present in the surrounding environment. It can be applied to any configuration that breaks the homogeneity of a perceptual field. So, we can analyze the complexity of this synthetic formation by tracing it back to increasingly simpler synthetic planes. When talking about composition, synthesis, and association, it has already the results of compositional operations and which provided the ultimate or primary material – either term suffices, depending on the direction we choose to take. These are essential factors in spatial orientation processes, but they are structurally opaque: we’ll work on three examples taken from classical music, ethnomusicology, and hearing aids.

DBMKA-Net:Dual branch multi-perception kernel adaptation for underwater image enhancement

In recent years, due to the dependence on wavelength-based light absorption and scattering, underwater photographs captured by devices often exhibit characteristics such as blurriness, faded color tones, and low contrast. To address these challenges, convolutional neural networks (CNNs) with their robust feature-capturing capabilities and adaptable structures have been employed for underwater image enhancement. However, most CNN-based studies on underwater image enhancement have not taken into account color space kernel convolution adaptability, which can significantly enhance the model’s expressive capacity. Building upon current academic research on adjusting the color space size for each perceptual field, this paper introduces a Double-Branch Multi-Perception Kernel Adaptive (DBMKA) model. A DBMKA module is constructed through two perceptual branches that adapt the kernels: channel features and local image entropy. Additionally, considering the pronounced attenuation of the red channel in underwater images, a Dependency-Capturing Feature Jump Connection module (DCFJC) has been designed to capture the red channel’s dependence on the blue and green channels for compensation. Its skip mechanism effectively preserves color contextual information. To better utilize the extracted features for enhancing underwater images, a Cross-Level Attention Feature Fusion (CLAFF) module has been designed. With the Double-Branch Multi-Perception Kernel Adaptive model, Dependency-Capturing Skip Connection module, and Cross-Level Adaptive Feature Fusion module, this network can effectively enhance various types of underwater images. Qualitative and quantitative evaluations were conducted on the UIEB and EUVP datasets. In the color correction comparison experiments, our method demonstrated a more uniform red channel distribution across all gray levels, maintaining color consistency and naturalness. Regarding image information entropy (IIE) and average gradient (AG), the data confirmed our method’s superiority in preserving image details. Furthermore, our proposed method showed performance improvements exceeding 10% on other metrics like MSE and UCIQE, further validating its effectiveness and accuracy.

Efficient analysis of deep neural networks for vision via biologically-inspired receptive field angles: An in-depth survey

Efficient feature extraction is a pivotal requirement for Deep Neural Network (DNN) models, particularly in the realm of visual tasks where effective feature extraction relies on well-designed receptive fields. Nevertheless, the evaluation and fulfillment of the criteria for “effective receptive fields”, along with their essential conditions, continue to pose numerous unresolved mysteries. In this Analysis we amalgamated principles from the field of biologically inspired vision to reevaluate the role of receptive fields within neural networks, and have proposed a comprehensive design theory aimed at characterizing and guiding the research of efficient DNNs. Our design theory draws from both the principles of biological vision and the latest advances in DNN design theory. It encompasses five fundamental axes of receptive field design research: the Global Artificial Neuron Workspace (GANW), Layered Processing Theory (LPT), Multi-Receptive Field Integration Design (M-RFID), Heterogeneous Receptive Field (HRF), and Receptive Field Plasticity Theory (RFPT). We have applied these design theories to validate traditional and cutting-edge neural networks, conducting in-depth analyses of the outcomes to provide a summary of the current landscape of research on visual receptive fields. Furthermore, we have classified current visual receptive fields into three categories: sample-specific local receptive fields, global spatial receptive fields, and adaptive dynamic receptive fields. From the perspective of perceptual fields and network lightweight design, we have introduced some classic and state-of-the-art DNN models, with a particular emphasis on those employing innovative algorithms that have yielded the most recent achievements. We have concluded by highlighting the key areas of interest in the field of visual receptive fields for future research endeavors.

The Evolution of Symbolic Thought: At the Intersection of Schizophrenia Psychopathology, Ethnoarchaeology, and Neuroscience.

The human capacity for symbolic representation arises, evolutionarily and developmentally, from the exploitation of a widespread sensorimotor network, along a fundamental continuity between embodied and symbolic modes of experience. In this regard, the fine balancing between constrained sensorimotor connections (responsible for self-embodiment processing) and more untethered neural associations (responsible for abstract and symbolic processing) is context dependent and plastically neuromodulated, thus intersubjectively constructed within a specific socio-cultural milieu. Instead, in the schizophrenia spectrum this system falls off catastrophically, due to an unbalance toward too unconstrained sensorimotor connectivity, leading to a profound distortion of self/world relation with a symbolic activity detached from its embodied ground. For this very reason, however, schizophrenia psychopathology may contribute to unveil, in a distorted or magnified way, ubiquitous structural features of human symbolic activity, beneath the various, historically determined cultural systems. In this respect, a comparative approach, linking psychopathology and ethnoarchaeology, allows highlight the following invariant formal characteristics of symbolic processing: (1) Emergence of salient perceptive fragments, which stand out from the perceptual field. (2) Spreading of a multiplicity of new significances with suspension of common-sense meaning. (3) Dynamic and passive character through which meaning proliferation is experienced. This study emphasizes the importance of fine-grained psychopathology to elucidate, within a cross-disciplinary framework, the evolutionarily and developmental pathways that shape the basic structures of human symbolization.

Underwater small target detection under YOLOv8-LA model

In the realm of marine environmental engineering, the swift and accurate detection of underwater targets is of considerable significance. Recently, methods based on Convolutional Neural Networks (CNN) have been applied to enhance the detection of such targets. However, deep neural networks usually require a large number of parameters, resulting in slow processing speed. Meanwhile, existing methods present challenges in accurate detection when facing small and densely arranged underwater targets. To address these issues, we propose a new neural network model, YOLOv8-LA, for improving the detection performance of underwater targets. First, we design a Lightweight Efficient Partial Convolution (LEPC) module to optimize spatial feature extraction by selectively processing input channels to improve efficiency and significantly reduce redundant computation and storage requirements. Second, we developed the AP-FasterNet architecture for small targets that are commonly found in underwater datasets. By integrating depth-separable convolutions with different expansion rates into FasterNet, AP-FasterNet enhances the model’s ability to capture detailed features of small targets. Finally, we integrate the lightweight and efficient content-aware reorganization (CARAFE) up-sampling operation into YOLOv8 to enhance the model performance by aggregating contextual information over a large perceptual field and mitigating information loss during up-sampling.Evaluation results on the URPC2021 dataset show that the YOLOv8-LA model achieves 84.7% mean accuracy (mAP) on a single Nvidia GeForce RTX 3090 and operates at 189.3 frames per second (FPS), demonstrating that it outperforms existing state-of-the-art methods in terms of performance. This result demonstrates the model’s ability to ensure high detection accuracy while maintaining real-time processing capabilities.

Enhanced Remote Sensing Image Compression Method Using Large Network with Sparse Extracting Strategy

Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the encoder. This ability bias results in likelihood features that fail to extract sufficient information from latent representations. To solve this problem, the feature extraction capabilities of the hyper-encoder are enhanced to better estimate the Gaussian likelihood of the latent representation in end-to-end network optimization. Specifically, residual blocks and a parameter estimation module are incorporated to balance the performance of the encoder and the hyper-encoder. Furthermore, it is observed that the well-trained compression model tends to generate a fixed pattern of latent representations. Therefore, we incorporate a nonlocal cross-channel graph (NCG) on the backside of the encoder. Specifically, it aggregates features between similar latent representations in a graphical manner to further enhance the side information extraction capability of the hyper-encoder. Considering the computational cost, a sparse graph strategy is further developed to dynamically select the most relevant latent representations for aggregation operations, which greatly reduces the computational effort. The proposed algorithm is named nonlocal cross-channel efficient graph (NCEG). A long-dependent residual network is selected as the backbone, and a sparse attention module is inserted into the encoder/decoder side to enhance the perceptual field of the network. The experimental results on two evaluation datasets demonstrate that the proposed method achieves satisfactory results compared to other learning-based methods.

VisioSignNet: A Dual-Interactive Neural Network for enhanced traffic sign detection

Effective traffic sign detection is crucial for the safety and operational efficiency of autonomous vehicle navigation systems, particularly in dynamically changing environments. Addressing the primary challenges of long-range pixel dependencies and enhancing the detectability of small objects in complex scenes, we present VisioSignNet: A Dual-Interactive Neural Network designed for enhanced traffic sign detection. This architecture incorporates Local and Global Interactive Modules (LGIM) and Enhancing Channel and Space Interaction (ECSI) modules. The LGIM is engineered to balance local and global feature interactions, while the ECSI optimizes the interchange of information across channel and spatial dimensions. Their synergistic interaction not only enhances the perceptual field at early processing stages but also significantly improves the recognition of small-scale, critical traffic signs. Evaluated on the TT100K and GTSDB datasets, VisioSignNet achieved mean average precision (mAP) scores of 90.5% and 97%, respectively, with a model size of 26M parameters. Its enhanced variant, VisioSignNet_l, with 34M parameters, reached mAP scores of 93.2% and 97.8%. These outcomes substantiate VisioSignNet’s efficacy in tackling the complexities of traffic sign detection, confirming its potential as a robust solution in the field of autonomous driving technologies.

Target Detection of Diamond Nanostructures Based on Improved YOLOv8 Modeling.

Boron-doped diamond thin films exhibit extensive applications in chemical sensing, in which the performance could be further enhanced by nano-structuring of the surfaces. In order to discover the relationship between diamond nanostructures and properties, this paper is dedicated to deep learning target detection methods. However, great challenges, such as noise, unclear target boundaries, and mutual occlusion between targets, are inevitable during the target detection of nanostructures. To tackle these challenges, DWS-YOLOv8 (DCN + WIoU + SA + YOLOv8n) is introduced to optimize the YOLOv8n model for the detection of diamond nanostructures. A deformable convolutional C2f (DCN_C2f) module is integrated into the backbone network, as is a shuffling attention (SA) mechanism, for adaptively tuning the perceptual field of the network and reducing the effect of noise. Finally, Wise-IoU (WIoU)v3 is utilized as a bounding box regression loss to enhance the model's ability to localize diamond nanostructures. Compared to YOLOv8n, a 9.4% higher detection accuracy is achieved for the present model with reduced computational complexity. Additionally, the enhancement of precision (P), recall (R), mAP@0.5, and mAP@0.5:0.95 is demonstrated, which validates the effectiveness of the present DWS-YOLOv8 method. These methods provide effective support for the subsequent understanding and customization of the properties of surface nanostructures.

An efficient motion visual learning method for video action recognition

Currently, efficient spatio-temporal information modeling is one of the key research components to solve the action recognition problem. Previous approaches focus on enhancing the backbone features individually using hierarchical structures, and unfortunately, most of them fail to achieve a better balance between the interactional adequacy of features within the structure. In this work, we propose an effective Multi-dimensional Adaptive Fusion Network (MDAF-Net), which can be embedded into the mainstream action recognition backbone in a plug-and-play manner to fully activate the transfer and representation of action features in the deep network. Specifically, our MDAF-Net contains two main components: the Adaptive Temporal Capture Module (ATCM) and the Extended Spatial and Channel Module (ESCM). The ATCM effectively suppresses the over-expression of similar features in adjacent frames and activates the expression of motion flow information. The ESCM further improves temporal modeling efficiency by extending the spatial feature perceptual field and enhancing channel attention. Extensive experiments on several challenging action recognition benchmarks, such as Something-Something V1&V2 and Kinetics-400, demonstrate that the proposed MDAF can achieve state-of-the-art and competitive performance.

Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction

Abstract Accurately predicting the remaining useful life of aerospace engines is crucial for enhancing the reliability of aviation equipment. While some methods have taken note of the challenges posed by vast sensor data and complex signal interrelationships, there is still room for improvement in performance. This paper proposes a novel deep learning model that utilizes a parallel structure to independently process inputs from various sensor signals. Each branch in this parallel structure employs a combination of an improved Inception module and a novel feature filtering module as a feature extractor. The improved Inception module boasts a larger perceptual field to ensure the integrity of feature information. The feature filtering module calculates the importance weights of feature information through convenient computation, allowing the network to focus more on feature information without significantly increasing computational complexity. Finally, the feature extractor is combined with a gated recurrent unit module to learn features from sensor signals. Extensive experiments were conducted on the C-MAPSS standard dataset, comparing the proposed method with other state-of-the-art methods. Ablation experiments were performed on the new generation N-CMAPSS standard dataset. The results of the experiments confirm the superiority and rationality of the proposed prediction method.

Field cabbage detection and positioning system based on improved YOLOv8n

BackgroundPesticide efficacy directly affects crop yield and quality, making targeted spraying a more environmentally friendly and effective method of pesticide application. Common targeted cabbage spraying methods often involve object detection networks. However, complex natural and lighting conditions pose challenges in the accurate detection and positioning of cabbage.ResultsIn this study, a cabbage detection algorithm based on the YOLOv8n neural network (YOLOv8-cabbage) combined with a positioning system constructed using a Realsense depth camera is proposed. Initially, four of the currently available high-performance object detection models were compared, and YOLOv8n was selected as the transfer learning model for field cabbage detection. Data augmentation and expansion methods were applied to extensively train the model, a large kernel convolution method was proposed to improve the bottleneck section, the Swin transformer module was combined with the convolutional neural network (CNN) to expand the perceptual field of feature extraction and improve edge detection effectiveness, and a nonlocal attention mechanism was added to enhance feature extraction. Ablation experiments were conducted on the same dataset under the same experimental conditions, and the improved model increased the mean average precision (mAP) from 88.8% to 93.9%. Subsequently, depth maps and colour maps were aligned pixelwise to obtain the three-dimensional coordinates of the cabbages via coordinate system conversion. The positioning error of the three-dimensional coordinate cabbage identification and positioning system was (11.2 mm, 10.225 mm, 25.3 mm), which meets the usage requirements.ConclusionsWe have achieved accurate cabbage positioning. The object detection system proposed here can detect cabbage in real time in complex field environments, providing technical support for targeted spraying applications and positioning.

Less-is-more: auditory strategies for reduced reality

AbstractIn this paper, we propose a reduced reality [RR] concept of less-is-more that virtual reality [VR] designers can use to create technological frameworks which reduce sensory overload and allow for better concentration and focus, less stress, and novel scenarios. We question the approach taken by scholars in the field of extended reality [XR] research, where the focus is typically to design and use technology that adds sensory information to the user’s perceptual field, and we address some of the confusion related to the typical uses of the term reality. To address the latter terminological muddle, we define reality as our conscious experience of the environment, an emergent perception, and we use this definition as the basis for a discussion on the role of sound in balancing sensory information and in the construction of less cluttered and less stressful perceptual environments. We further discuss current auditory strategies that reduce and alter auditory stimuli, and we suggest potential future strategies for RR that use crossmodal design approaches, masking, and filtering techniques.

Solar Surface Defect Detection Based on Optimized YOLOv5

This study introduces an advanced Solar Cell Surface Defect Detection method utilizing an improved YOLO v5, FaserRCNN and YOLOV6 algorithms. Addressing the challenges posed by complex image backgrounds, variable defect morphology, and large-scale differences, our approach incorporates deformable convolution in the CSP module for adaptive learning scale and perceptual field size. The integration of the ECA-Net attention mechanism enhances feature extraction capabilities, while the addition of a tiny defect prediction head improves detection accuracy across different scales. Optimization techniques, including Mosaic and MixUp data augmentation, K-meansCC clustering anchor box algorithm, and the CIOU loss function, contribute to superior model performance. Experimental results demonstrate an impressive accuracy of 97.14% for YOLOv5, outperforming Faster R-CNN's 90.66%. Further extension studies on YOLOv6, YOLOv7, and YOLOv8 reveal YOLOv6 as the most effective, achieving a remarkable accuracy of 98.28%. This research establishes a robust solution for solar cell defect detection, showcasing the efficacy of our proposed algorithm for industrial applications.

Global context aware dual channel pyramid model for robust image shadow removal

Deep learning has made significant advancements in shadow removal. However, current methods primarily focus on local operations, leading to artifacts around shadow boundaries and inconsistent lighting between shadow and non-shadow regions. To address this issue, a global context aware dual channel pyramid model is proposed for robust image shadow removal in this paper. The model leverages a multi-scale channel attention framework based on transformers to capture global information. It consists of a shadow detection module and a dual-channel shadow interaction module to utilize non-shadow regions in aiding shadow restoration. Additionally, Winograd-based separable convolution attention shadow interaction attention is proposed to effectively expand the perceptual field and facilitate comprehensive utilization of contextual relevance between shadow and non-shadow regions. Extensive experiments are conducted on several popular public datasets such as ISTD, SRD and ISTD + to evaluate the effectiveness of our model.

Parallel feature enhancement and adaptive weighted feature fusion for semantic segmentation

To tackle the challenges posed by the insensitivity of current multi-scale networks to image detailed information and their limited capacity to model contextual relationships, our paper proposes a novel semantic segmentation network called LEFNet. It is based on Parallel Feature Enhancement (PFE) and Adaptive Weighted Feature Fusion (AWFF). In the coding stage, the PFE module performs posterior enhancement of multi-scale features by Detail Sharpening Attention (DSA) and High-level Dilation Fusion (HDF) methods. The DSA guides the learning of detailed information in low-level features. The HDF broadens the perceptual field, enabling the acquisition of richer high-level features. In the decoding stage, the AWFF module supersedes the conventional feature fusion methods. The AWFF module constructs perceptual factors for each multi-scale feature map, enabling weighted learning of features. It emphasizes features with stronger semantic information, making them more decisive in pixel classification. It integrates features more reasonably based on the relevance of global contextual information, fully releasing the expressive potential of encoded features. Our method achieves mIoU scores of 82.8%, 49.3%, and 45.4% on the Cityscapes, ADE 20 K, and COCO-Stuff 164 K datasets, respectively, reaching an advanced level on popular benchmarks. The experimental results show that LEFNet alleviates the challenge of insensitivity of multi-scale networks to image detailed information, improves its ability to model contextual relationships, and significantly improves segmentation performance.

Multi-fidelity deep learning for aerodynamic shape optimization using convolutional neural network

Aerodynamic shape design is essential for improving aircraft performance and efficiency. First, this study introduces a data-driven optimization framework utilizing a multi-fidelity convolutional neural network (MFCNN) for aerodynamic shape optimization. To achieve better optimization results with reduced computational cost, the framework dynamically incorporates new data in each optimization cycle. Specifically, it constantly involves the optimal solution from previous cycle as a new high-fidelity sample and employs a low-fidelity infilling strategy that maximizes the minimum Euclidean distance for selecting new low-fidelity samples. Moreover, a standard synthetic benchmark is used to elaborate the procedure of optimization and show the capability and effectiveness of the framework. Finally, the framework is applied to two aerodynamic shape optimization problems: maximizing the lift-to-drag ratio for the Royal Aircraft Establishment 2822 (RAE2822) airfoils and minimizing the cruise drag coefficient for the three-dimensional (3D) drooped and scarfed non-axisymmetric nacelles. The framework increases the lift-to-drag ratio by 51.21% over the baseline and achieves an 18.79% reduction in the cruise drag coefficient for nacelle optimization, outperforming traditional multi-fidelity deep neural network optimization framework. Sufficiently utilizing the implicit relations between different fidelity levels of data through defined local perceptual fields and convolution, our MFCNN-based optimization framework signifies a step forward in the efficiency and accuracy of aerodynamic shape optimization.

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at https://github.com/Ma-Xinzhi/EEG-TransNet.