UUVDNet: An efficient unmanned underwater vehicle target detection network for multibeam forward-looking sonar

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.

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.

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.

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds.

Breast cancer is one of the most common malignant tumors among women worldwide, and early diagnosis is crucial for improving patient survival rates. This study proposes an improved YOLOv8-based mass detection model for mammographic images, referred to as Mass Detection YOLOv8 (MD-YOLOv8). To address the limitations of existing detection models—such as insufficient feature extraction and a high miss rate for small targets—this work integrates the Convolutional Block Attention Module (CBAM) and the Content-Aware ReAssembly of Features (CARAFE) into the original YOLOv8 architecture to enhance its feature representation and small mass detection capabilities. Specifically, CBAM is introduced before the Upsample and CBS layers in the Neck of the network to adaptively recalibrate the feature maps along channel and spatial dimensions, thereby improving the model’s sensitivity to fine-grained details and enhancing its focus on mass regions in mammographic images. In addition, the original upsampling module of YOLOv8 is replaced with the CARAFE module, which strengthens multi-scale feature fusion and optimizes feature reconstruction through a content-aware upsampling strategy, ultimately improving the model’s ability to detect masses. The proposed model is evaluated on the public CBIS-DDSM mammographic image dataset, achieving a mean Average Precision (mAP@0.5) of 0.491, which represents a 3.2% improvement over the baseline YOLOv8 model.

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

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.

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification.

Real-time performance and accuracy are the standards to measure the detection algorithm performance. The existing detection methods for forward-looking sonar target image are difficult to achieve ideal balance between the two standards. Due to the influence of background interference and non-rigid deformation, it hardly obtains the high detection accuracy in fast detection. In this paper, we propose a Mobilenetv3-YOLOv4-Sonar algorithm, which is a more accurate and lightweight target detection model. The backbone of YOLOv4 adopts Mobilenetv3, which is improved by Convolutional Block Attention Module (CBMA) and modified Squeeze-and-Excitation Network (SENet). Consequently, we can eliminate the influence of high-light background interference and reduce the complexity of the model. Meanwhile, the improved SPPnet is used as the neck of YOLOv4 to reduce the failure of target detection caused by non-rigid deformation. Extensive experiments and comparisons with state-of-arts detectors illustrate that the accuracy of our model has increased by 4.6% compared with YOLOv4 on the dataset, and the model parameters are only one tenth of the traditional YOLOv4, which has practical significance for target detection in forward-looking sonar images.

Ship target detection on the sea surface is one of the most common scenes in the field of target detection. The accuracy of target detection plays an important role in the field of sea rescue and defense. However, complex infrared sea surface scenes, such as island shore, fish scale wave, sea surface bright band and other disturbances, bring great challenges to target detection. In this paper, we propose an improved YOLOV5 model by analyzing the characteristics of infrared image imaging and ship target on the sea surface. Aiming at the problem of information loss of small targets in the deep layer of the network, we redesigned the backbone network, which was composed of four Multi-scale residual blocks, and each block was connected by CBAM (Convolutional Block Attention Module) attention mechanism to improve information transmission and fusion between different feature layers. Aiming at the problem of complex sea surface scene interference, we use FPN and PAN (Feature Pyramid Network and Personal Area Network) mechanism to construct feature fusion network in neck, and add shallow feature fusion in FPN. Our model achieves good performance both in terms of latency and accuracy. In a self-collected sea surface scene dataset, multiple SOTAs for detection tasks are compared and the results demonstrate the superiority of the proposed method.

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.

MC-ShuffleNetV2: A lightweight model for maize disease recognition

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.