MC-ShuffleNetV2: A lightweight model for maize disease recognition

Weeds are one of the primary factors that reduce crop productivity by competing for nutrients and water, causing the plant to lose weight and resulting in reduced grain yield. Traditional agricultural practices often rely on uniform herbicide application, which can contaminate soil and raise costs. On agricultural land, selective weed treatment are an efficient and cost-effective way to control weeds that require a deep learning-based crop and weed segmentation system. Many existing crop and weed segmentation research works focus on achieving precise crop and weed segmentation results, rather than building lightweight models to deploy on edge devices. To attain this, we develop an effective and efficient convolutional neural network, namely the Concatenated Attention U-Net with Convolutional Block Attention Module (CAUC). By integrating Linear Concatenated Blocks (LCB), Attention Gate (AG) connections, and Convolutional Block Attention Module (CBAM), the proposed model efficiently utilizes feature maps among its architectural components to achieve superior performance. Depth-wise convolution layers and 1 × 1 convolution layers in LCBs reduce computational complexity. To enable the proposed model to identify the weed portions in multiple crop fields, we integrated three datasets in this research work, namely the Crop/Weed Field Image Dataset (CWFID), Sugar Beet, and Sunflower datasets. Experimental results on carrot, sugar beet, and sunflower crop datasets demonstrate high Accuracy (99.09%), MIoU (81.02%), and F1-score (99.06%), with a modest model size (5.6 MB) and computational parameters (0.377 million). We developed a lightweight computer vision application (13.7 MB) to demonstrate the model’s efficacy on low-computational devices.

Acquiring the growth information of tobacco plants quickly and accurately is an important prerequisite for their daily management. In view of the problem that the convolutional neural network is easily affected by background information, this study introduces the Convolutional Block Attention Module into the U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2-</sup> Net. This method is mainly composed of an encoder and a decoder. Before the feature map is decoded, it will go through the Convolutional Block Attention Module to filter more important features and inhibit the invalid features. In the decoder, in order to fuse the features of objects in different scales, the feature maps in the decoder are up-sampled and then concatenated. This information aggregation solves the problem of difficult object detection at different scales to some extent. The results of this method are compared with four current mainstream deep learning models(U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net, DeeplabV3+, PSP-Net, U-Net). The result shows: 1) The U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net network based on the Convolutional Block Attention Module has the highest extraction accuracy for the UAV visible light remote senseing of tobacco(accuracy was 98.53%, recall was 96.00%,F1-Score was 96.38%, kappa coefficient was 0.95, IOU was 93.02%); 2)In the extraction of the three test areas, the extraction value of this method is highly consistent with the label area value(the area ratios of a total of 13 plants in the three regions were kept between 90% and 100%),the effect is much higher than that of DeeplabV3+ and PSP-Net, and slightly higher than the extraction effect of U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net and U-Net. 3)In the segmentation of three typical canopy closure regions of A, B, and C, most of the accuracy factors in different regions can reach 94% and above. It can be seen that the method in this study has certain advantages in feature extraction and has certain value in practical application.

As the importance of preserving and utilizing industrial heritage continues to grow, improving the efficiency and accuracy of damage detection for industrial heritage has become a key research focus. This work optimizes the structure of the traditional AlexNet HSD (Alex Krizhevsky Network Hierarchical Structure Detection) model. By integrating the Convolutional Block Attention Module (CBAM) and Support Vector Machine (SVM), an AlexNet HSD + CBAM + SVM (AlexNet HCS) model is proposed to enhance the performance of industrial heritage damage detection. Experiments are conducted on a comprehensive dataset composed of the xView2 Building Damage Assessment Dataset (xBD) and photos of third-line construction buildings in Southwest China. The results show that through structural improvements and the combination of the CBAM module and SVM, the AlexNet HCS model achieves an accuracy of 95.7%, an increase of 12.2% compared with AlexNet HSD. Its Precision, Recall, and F1 score are 94.8%, 95.7%, and 95.2% respectively, verifying the effectiveness of the optimization strategy. Ablation experiments verify the improvement of network structure and the synergistic gain of CBAM and SVM. CBAM only increases 3.5% Floating Point Operations (FLOPs) and 4ms reasoning delay, but brings 1.8% accuracy improvement; Placing DropBlock in Conv5 can further inhibit over-fitting. In comparative experiments with other models, AlexNet HCS demonstrates superior classification performance and faster convergence speed, proving its efficacy in building damage identification. Moreover, based on the findings in damage detection, this work proposes specific pathways for the adaptive reuse of industrial heritage from the Third Front Construction in Southwest China. It aims to support the sustainable development and cultural preservation of industrial heritage. This work intends to provide novel technical support and theoretical foundation for the protection of industrial heritage, promoting its scientific and sustainable utilization.

A detection method for dead caged hens based on improved YOLOv7

With the continuous development of deep learning technologies, fault prediction techniques based on various neural networks have been evolving. The deep learning modules based on U-Net residual networks have shown significant advantages in both learning efficiency and effectiveness. In this paper, we propose a deep learning model that integrates a 3D U-Net residual architecture, Convolutional Block Attention Module (CBAM), and Multi-scale Enhanced Global Attention (MEGA) module for automatic seismic fault detection and segmentation. This model can effectively handle complex 3D seismic data, fully exploiting both spatial and channel information, significantly improving the prediction accuracy for small faults, while only slightly increasing the computational cost.Firstly, the model uses the 3D U-Net as the backbone framework, where the residual blocks (BasicRes) extract features through multiple convolution layers. The CBAM module is incorporated to apply attention weighting, enhancing the model's ability to focus on critical information. The CBAM module combines channel attention and spatial attention, effectively adjusting the importance of feature maps from different dimensions, enabling the model to identify potential fault features in complex seismic data.Secondly, the MEGA module is introduced into the model, which further improves the model's feature representation ability by fusing multi-scale features and applying a global attention mechanism. By weighting global information, the MEGA module helps the model better capture key seismic fault features during feature fusion. This design allows the model to focus not only on local details but also to fully utilize the global contextual information in 3D data, thereby enhancing the accuracy of fault detection.After validation, the model achieved promising results in seismic fault detection tasks, automatically identifying and segmenting fault structures in seismic data. The accuracy was improved from 80% with the original 3D U-Net residual network to 85%-87%. This provides strong support for applications such as seismic exploration and subsurface imaging.Keywords: Seismic Fault Detection, 3D U-Net, Convolutional Block Attention Module (CBAM), Multi-scale Enhanced Global Attention (MEGA), Deep Learning

The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results.

Traditional microarray scanners typically rely on high-performance computers to process the acquired images. To address these challenges, we propose a novel microarray detection (MD) method designed for deployment on a charge-coupled device (CCD) microarray scanner integrated with an edge computing. In this MD method, we propose a novel model named C2f_SC, which integrates the computational efficiency of the star operation with contextual anchor attention to enhance detection accuracy within a compact model architecture. We further integrate the C2f_SC module into the You Only Look Once v8 (YOLOv8) framework, utilizing MobileNetV3 with the convolutional block attention module (CBAM) as the backbone, removing the detection heads for medium and large objects, replacing the bounding box loss function with the signed intersection over union, and incorporating ResBlock_CBAM before the small object detection head. With the microarray images from the gene expression omnibus database, experimental results demonstrate that our method significantly reduces the required giga floating point operations per seconds (GFLOPs) and model size compared to other YOLO models, while maintaining comparable or higher mAP@50 performance with only a slight drop at mAP@50–95. Specifically, against YOLOv8n, our approach matches mAP@50 while using just 32.9% of GFLOPs and 43.3% of model weights. In addition, we conducted a comparative experiment between the laser confocal scanner and the CCD scanner on the BAC Microarray from MGmed Inc., Republic of Korea (MGMED BAC) to verify the reliability of the CCD scanner. Extensive experimental results demonstrate that the proposed MD method significantly reduces reliance on large-scale computing systems.

Motivated by the increasing popularity and success of attention mechanisms, we observe that popular convolutional attention models like Squeeze-and-Excite (SE) and Convolutional Block Attention Module (CBAM) rely on expensive multi-layer perceptron (MLP) layers. These MLP layers significantly increase computational complexity, making such models less applicable to 3D image contexts, where the data dimensionality and computational costs are inherently higher. In 3D medical imaging, such as pulmonary CT scans, efficient processing is crucial due to the large volume of data. Traditional 2D attention mechanisms, when generalized to 3D, increase the computational load, creating a demand for more efficient attention mechanisms that can operate effectively within a 3D tasks. In this work, we investigate the possibility of incorporating fully convolutional attention within 3D context. Particularly, we present two 3D fully convolutional attention blocks in which we demonstrate effectiveness within the 3D context. We demonstrate effectiveness of the proposed method using the pulmonary CT scans for 3D lung nodule detection. Building on existing 3D nodule detection methods, we present AttentNet, an automated lung nodule detection framework from CT images that performs detection as an ensemble of two stages, candidate proposal stage (Region Proposal Network), in which a high number of candidates is produced, and a false positive reduction stage to reduce the number of false alarms. Throughout our work, we compare the proposed 3D convolutional attention blocks to a number of popular 2D convolutional attention mechanisms by generalizing them to 3D modules, including SE units and CBAM channel attention and spatial attention units. We also compare these to Transformer self-attention units. For the False positive reduction stage, we incorporate a joint analysis approach that considers the variable nodule morphology by aggregating spatial information from different contextual levels. We use LUNA-16 lung nodule detection dataset to demonstrate the benefits of using the proposed fully convolutional attention blocks comparing to baseline popular lung nodule detection methods where no attention is used. It is worth noting that our work does not aim at achieving state-of-the-art results in the lung nodule detection task, rather to demonstrate the benefits of incorporating fully convolutional attention within a 3D context.

MRI image classification of brain tumors is critical for accurate and early diagnosis. New developments in deep learning have revealed that inserting attention mechanisms into convolutional neural networks can greatly improve classification performance. This work assesses the effectiveness of Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) integrated with the ResNet50 model for brain tumor classification. The SE module recalibrates channel-wise feature responses, while CBAM enhances feature representation by combining channel and spatial attention. The SE and CBAM are combined with ResNet50, greatly increasing classification accuracy, precision, and recall when compared to the basic model, according to experimental results on MRI datasets. The suggested model employs attention mechanisms to focus valuable information selectively and suppress irrelevant information. The experiments on publicly available brain tumor datasets display great improvements over basic CNN models, with precision, recall, accuracy, as well as F1 score at 0.9914, 0.9903, 0.9945, and 0.9908, respectively. From these results, the importance of attention mechanisms in deep learning models for medical imaging is highlighted, which suggests that SE and CBAM modules can be available as more dependable and effective instruments for brain tumor classification in clinical settings. Future studies should investigate transformer-based and hybrid attention techniques to enhance automated brain tumor categorization.

Artificial Intelligence (AI) based ultrasound image processing is quite important for diagnosis of renal tumors. Traditional networks, however, struggle with accurate lesion segmentation and differentiation between benign and malignant cases in complex ultrasound images. This paper introduces an advanced multitask network model to overcome these limitations. Our model combines a deep separable Convolutional ResNet with a NestedUNet, enriched with a Convolutional Block Attention Module (CBAM) and residual connections, enhancing its precision in lesion segmentation and classification in intricate images. Compared with the existing methods, the model proposed in this study has the following innovative contributions: (1) combining depthwise separable convolution and NestedUNet, the computational efficiency and feature extraction ability of the model are significantly improved; (2) The Convolutional Block Attention Module (CBAM) is introduced to enhance the attention to key features, to effectively distinguish lesions in complex backgrounds; (3) The gradient flow is improved by the residual connection, which ensures the stable training of the deep network. Experimental results show that the IoU of the model on the kidney tumor dataset reaches 94.23%, which is significantly better than that of STUNet (92.31%). These results show that the model proposed in this study has significant advantages in processing complex ultrasound images, providing a more accurate basis for clinical diagnosis, and has a high application potential.

Modulation recognition of single‐component radar signals has been widely studied in the fewer years. Meanwhile, as the electromagnetic environment in the actual battlefields is ever‐increasingly complicated, the demand for the modulation recognition of multi‐component radar signals is also increasing. However, the modulation recognition of single‐component radar signals performs poorly on multi‐component radar signals. To address this problem, a novel modulation recognition method of multi‐component radar signals based on the deep convolutional neural network with a convolutional block attention module (DCNN‐CBAM) is proposed, and the network architecture mainly includes mobile‐inverted bottleneck convolution (MBConv), Fused‐MBConv, and convolutional block attention module (CBAM). An adaptive optimization algorithm Adam with weighted gradient and dynamic bound is proposed for accelerating convergence speed and generalisation performance. The proposed method can not only recognise dual‐component and three‐component radar signals but also four‐component radar signals. The experimental results demonstrate that the recognition accuracies of all radar signals modulation types are more than 83.57% at −8 dB for dual‐component radar signals, 67.41% for three‐component radar signals, and 57.95% for four‐component radar signals, which verify the better recognition performance of DCNN‐CBAM for multi‐component radar signals. The recognition accuracy of the proposed DCNN‐CBAM for dual‐component radar signals can be up to 95.95% at −6 dB, which shows superior recognition performance over the residual method. This work provides an essential guidance for enhancing the recognition performance of multi‐component radar signals.

Brain-computer interfaces (BCI) allow for the brain to communicate with electronic devices. Concerning the BCI paradigms, motor imagery uses brain signals to decode an imagined movement. However, this is a hard task given the low signal-to-noise ratio. Usually, the main steps in BCI models are pre-processing, feature extraction, and classification. In recent years, Convolutional Neural Networks (CNNs) have been gaining relevance in several areas of science due to their feature extraction, translation invariance, and parameter sharing capa-bilities. Another, more recent way of feature extraction is using attention mechanisms, which are layers of neural networks based on human attention and have the ability to highlight important features. A variation of the attention mechanism is the Convolutional Block Attention Module, which combines the CNN structure with the attention mechanism. In this work, we propose a new model that joins the core architecture of EEGNet, a compact CNN widely used in the literature, with the Convolutional Block Attention Module and residual connections. The residual connections were introduced to lower data degradation throughout the model. The results highlight the residual connection's importance for the performance of the model. The proposed model obtained a kappa result 5.2% better than the EEGNet with a p-value less than 0.01 on BCI Competition IV dataset 2a, which is a well-known dataset for Motor Imagery. Furthermore, the proposal was better than EEGNet for most subjects and had the best-worst case.

Change detection with SAR images encounters more difficulties than optical ones due to the presence of the speckle noise. Recently, inspired by the human visual systems, self-attention mechanism is incorporated in large-scale classification tasks to improve the classification performance. Inspired by this, this paper proposed a novel change detection method from SAR images based on self-attention mechanism. First, training samples with pseudo-labels are generated by the log-ratio operator and hierarchical FCM algorithm. Image patches around these pixel are generated, and these patches are fed into a network with the convolutional block attention module (CBAM). After training, all the pixels from multitemporal SAR images are fed into the learned network, and then the final change map can be obtained. Experimental results on two real SAR datasets demonstrate the superiority of the proposed method over two closely related methods.

In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions .

Early diagnosis of eye diseases plays a critical role in treatment success and public health. With the widespread use of modern medical imaging methods, the development of automated diagnostic systems from retinal fundus images has become an important research area. In this study, the effects of integrating the Convolutional Block Attention Module (CBAM) into EfficientNetB0 and DenseNet121 architectures were investigated for the classification of cataract, diabetic retinopathy, glaucoma, and healthy subjects. Experimental results demonstrated that the CBAM attention mechanism enhances accuracy and generalization performance, particularly in distinguishing complex retinal findings. For DenseNet121, accuracy, precision, recall, and F1-score were obtained as 88.37%, 89.66%, 88.37%, and 88.52%, respectively. EfficientNetB0 achieved 96.32% accuracy, 96.34% precision, 96.32% recall, and 96.33% F1-score. After CBAM integration, the accuracy of DenseNet121 increased to 90.39% and its F1-score to 90.54%, while EfficientNetB0 improved to 96.56% accuracy and 96.57% F1-score. These results reveal that the incorporation of CBAM enhances the performance of deep learning models and significantly contributes to the development of reliable and clinically applicable systems for the automated detection of eye diseases