Channel Attention Module Research Articles

Automated pixel-level crack detection and quantification using deep convolutional neural networks for structural condition assessment

Crack detection is a crucial task in assessing the condition of concrete structures. Herein, a novel deep learning method based on convolutional neural networks, referred to as R-FPANet, is proposed for crack detection. The R-FPANet performs automatic segmentation and quantification of crack morphology at the pixel level. In this methodology, the modularization concept based on the following three modules is adopted: ResNet-50 is chosen as the backbone to extract features from images, the Feature Pyramid Network with Dense Block is integrated to promote the fusion of both shallow and deep features as well as enhance feature reuse, and self-attention mechanisms such as Channel Attention Module and Position Attention Module are introduced to strengthen the dependency between features. Based on the crack segmentation results, a suitably established framework is developed for quantitative analysis of the major geometric parameters, including crack area, crack length, crack mean width and crack max-width at the pixel level. To verify the effectiveness of the proposed method, a large-scale concrete crack image dataset was produced and carefully labeled at the pixel level and then utilized to train the model. Finally, our experiments reveal that the proposed approach achieves an Intersection over Union of 83.07%, further indicating that the segmentation performance of the proposed method is better than the state-of-the-art models and also confirming that the crack quantification results are close to reality. Overall, the proposed method performs well, contributing to crack detection and quantification with great potential for practical use.

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

AbstractAutomatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state‐of‐the‐art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi‐scale‐aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor‐based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature‐based method achieved the highest performance for survival days prediction over the state‐of‐the‐art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning‐based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days.

Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva

Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases.

Feature rectification and enhancement for no-reference image quality assessment

The assessment of image is a critical task in computer vision, particularly in a no-referenced context, which presents numerous challenges. Though Deep neural networks have exhibited excellent performance on no-reference quality assessment (NR-IQA), the distortion-aware NR-IQA methods are limited to specific distortion type and struggle to handle realistic distortion scenarios. In this work, we propose a feature rectification and enhancement convolutional neural network namely FREIQA for NR-IQA. Our approach targets to fully utilize the multi-stage semantic features to produce a well fused and rectified score vector for effective image quality regression. Specifically, the multi-stage semantic features extracted by a pre-trained backbone would be rectified with a multi-level channel attention module before integrating to a global score vector. The fused score vector then will be gradually optimized through the perception feature provided by the multi-stage semantic feature, and finally sent to quality regression network to obtain the image quality score. Experimental results on public benchmarks including LIVE, TID2013, CSIQ, LIVEC, KADID-10k, KonIQ-10k and Waterloo exploration database show that our FREIQA achieve the state-of-the-art performance.

The research of interference recognition method in multi-nodes cooperative frequency-hopping communication based on time–frequency image analysis and deep learning

Interference recognition has a significant and irreplaceable importance in keeping battlefield communication reliability and securing information safety. However, the existing recognition algorithms have a common disadvantage, that the model scale is normally very huge. In order to satisfy the demand of decreasing mobile device weight and limiting the memory space, this article designed a lightweight neural network based on the idea of structure reparameterization. This network will take the time–frequency image of the interference signal as the input of the convolutional neural network (CNN), and import the efficient channel attention module, efficiently extract the feature of the interference signals, thus benefiting the interference recognition. The simulation experiment indicates that the network has a high accuracy rate in interference recognition in the circumstances when the quantity of parameters is not huge. At the same time, in order to solve the problem of low recognition accuracy due to the limit of information, this article designed an interference recognition algorithm based on multi-nodes collaboration technology. Considering jamming-to-noise ratio (JNR) could differ for each node, this article set a node select module, that could select the nodes that contain a higher result of JNR as the input of the fusion algorithm when each JNR is highly different, which could adjust abnormal influence from each node. The simulation experiment indicates that, the interference recognition algorithm based on multi-nodes collaboration technology could efficiently increase interference recognize accuracy, and reduce the influence from low JNR nodes.

3D-FGAN: A 3D stochastic reconstruction method of digital cores

Digital core reconstruction can quantitatively characterize the interior microstructure of porous media and revealing variations in the petrophysical properties of rocks, which can be classified into physical experimental methods and numerical reconstruction methods. Numerical reconstruction methods utilize the information contained in 2D or 3D images to reconstruct 3D digital cores, which is relatively inexpensive and more efficient, but traditional numerical methods usually cannot reuse extracted statistical information from previous reconstruction process and may generate unsatisfying results. Nowadays, the development of deep learning has opened a door to the high-quality reconstruction of digital cores. Generative adversarial networks (GANs), viewed as an important branch of deep learning, have been used for the 3D digital core reconstruction. GANs have a strong feature extraction capability and can generate fake images that closely resemble the real ones. Nevertheless, due to the complex structure of 3D core images, especially those with high anisotropy, GANs and their variants still have difficulties in generating satisfying pore structure. To address the above issues, we propose a variant of GANs called 3D fast generative adversarial network (3D-FGAN) to rapidly generate high-quality 3D digital core images. Several channel attention modules called skip-layer excitation blocks are added to the generator of 3D-FGAN, enhancing the utilization of important features. Auto-encoders are employed to optimize the discriminator of 3D-FGAN, which makes the discriminator more comprehensively extract the features of input images and thus better guides the generator. Through the evaluation metrics of pore space distribution (e.g., isolated pore space and connected pore space, pore diameters and throat diameters), multiple-point connectivity, absolute permeability and diversity test, the quality of 3D-FGAN can be proved. Meanwhile, in the reconstruction of anisotropic shales with evident fractures, 3D-FGAN can quickly produce higher-quality images compared with some other numerical reconstruction methods.

Improved SiamCAR with ranking-based pruning and optimization for efficient UAV tracking

UAV tracking is a burgeoning task with vast application prospects in various fields such as agriculture, navigation, and public safety. However, the computational constraints and limited processing speed of drones hinder the deployment of Siamese tracking algorithms. In order to better apply tracking algorithms to drone devices, this paper proposes a novel Ranking-Based SiamCAR (RB-SiamCAR) tracker. The RB-SiamCAR tracker achieves model compression by utilizing a ranking-based filter pruning method, which sorts the filters in the backbone network based on their importance and prunes the filters with low ranks for efficient feature extraction. Additionally, considering existing Siamese trackers overlook the correlation between positive and negative samples and the coherence between classification and localization, we introduce two ranking-based losses. The classification ranking loss ensures that the ranking of positive samples is higher than that of hard negative samples, allowing the tracker to successfully select foreground samples without being fooled by distractors. The IoU-guided ranking loss aims to align the classification with the intersection over union (IoU) of the corresponding localizations of positive samples, enabling well-localized predictions to be represented by high classification confidence. To further enhance the tracking performance, we employ an effective channel attention module (ECA) that allows the network to automatically learn and focus on the most important channels for capturing more discriminative features. Experimental evaluations on UAV tracking benchmarks demonstrate that the proposed RB-SiamCAR outperforms existing state-of-the-art trackers. It is noteworthy that our RB-SiamCAR achieves an impressive tracking speed of nearly 100 fps. The experimental results validate its effectiveness and efficiency in UAV tracking applications.

Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method

The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention network (AKCA-Net) combined with an electronic nose (e-nose) to achieve soybean quality traceability. First, the e-nose system is used to collect soybean gas information from different origins. Second, depending on the characteristics of the gas information, we propose the adaptive convolutional kernel channel attention (AKCA) module, which focuses on key gas channel features adaptively. Finally, the AKCA-Net is proposed, which is capable of modeling deep gas channel interdependency efficiently, realizing high-precision recognition of soybean quality. In comparative experiments with other attention mechanisms, AKCA-Net demonstrated superior performance, achieving an accuracy of 98.21%, precision of 98.57%, and recall of 98.60%. In conclusion, the combination of the AKCA-Net and e-nose provides an effective strategy for soybean quality traceability.

Point Cloud Segmentation Network Based on Attention Mechanism and Dual Graph Convolution

To overcome the limitations of inadequate local feature representation and the underutilization of global information in dynamic graph convolutions, we propose a network that combines attention mechanisms with dual graph convolutions. Firstly, we construct a static graph based on the dynamic graph using the K-nearest neighbors algorithm and geometric distances of point clouds. This integration of dynamic and static graphs forms a dual graph structure, compensating for the underutilization of geometric positional relationships in the dynamic graph. Next, edge convolutions are applied to extract edge features from the dual graph structure. To further enhance the capturing ability of local features, we employ attention pooling, which combines max pooling and average pooling operations. Secondly, we introduce channel attention modules and spatial self-attention modules to improve the representation ability of global features and enhance semantic segmentation accuracy in our network. Experimental results on the S3DIS dataset demonstrate that compared to dynamic graph convolution alone, our proposed approach effectively utilizes both semantic and geometric relationships between point clouds using dual graph convolutions while addressing limitations related to insufficient local feature extraction. The introduction of attention mechanisms helps mitigate underutilization issues with global information, resulting in significant improvements in model performance.

Degradation trend prediction of rail stripping for heavy haul railway based on multi-strategy hybrid improved pelican algorithm

As a key component of the heavy-haul railway system, the rail is prone to damages caused by harsh operating conditions. To secure a safe operation, it is of great essence to detect the damage status of the rail. However, current damage detection methods are mainly manual, so problems such as strong subjectivity, lag in providing results, and difficulty in quantifying the degree of damage are easily generated. Therefore, a new prediction method based on the improved pelican algorithm and channel attention mechanism is proposed to evaluate the stripping of heavy-haul railway rails. By processing the rail vibration acceleration, it predicts the stripping damage degree. Specifically, a comprehensive health index measuring the degree of rail stripping is first established by principal component analysis and correlation analysis to avoid the one-sidedness of a single evaluation index. Then, the convolutional bidirectional gated recursive network is trained and generalized, and the pelican algorithm, improved by multiple hybrid strategies, is used to optimize the hyperparameters in the network so as to find the optimal solution by constantly adjusting the search strategy. The squeeze-excitation channel attention module is then incorporated to re-calibrate the weights of valid features and to improve the accuracy of the model. Finally, the proposed method is tested on a specific rail stripping dataset and a public dataset of PHM2012 bearings, and the generalization and effectiveness performance of the proposed method is proved.

Action Recognition Based on Multi-Level Topological Channel Attention of Human Skeleton.

In action recognition, obtaining skeleton data from human poses is valuable. This process can help eliminate negative effects of environmental noise, including changes in background and lighting conditions. Although GCN can learn unique action features, it fails to fully utilize the prior knowledge of human body structure and the coordination relations between limbs. To address these issues, this paper proposes a Multi-level Topological Channel Attention Network algorithm: Firstly, the Multi-level Topology and Channel Attention Module incorporates prior knowledge of human body structure using a coarse-to-fine approach, effectively extracting action features. Secondly, the Coordination Module utilizes contralateral and ipsilateral coordinated movements in human kinematics. Lastly, the Multi-scale Global Spatio-temporal Attention Module captures spatiotemporal features of different granularities and incorporates a causal convolution block and masked temporal attention to prevent non-causal relationships. This method achieved accuracy rates of 91.9% (Xsub), 96.3% (Xview), 88.5% (Xsub), and 90.3% (Xset) on NTU-RGB+D 60 and NTU-RGB+D 120, respectively.

A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid.

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.

Quark/gluon discrimination and top tagging with dual attention transformer

Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning techniques. In this study, we introduce a new architecture for jet tagging: the particle dual attention transformer (P-DAT). This novel transformer architecture stands out by concurrently capturing both global and local information, while maintaining computational efficiency. Regarding the self attention mechanism, we have extended the established attention mechanism between particles to encompass the attention mechanism between particle features. The particle attention module computes particle level interactions across all the particles, while the channel attention module computes attention scores between particle features, which naturally captures jet level interactions by taking all particles into account. These two kinds of attention mechanisms can complement each other. Further, we incorporate both the pairwise particle interactions and the pairwise jet feature interactions in the attention mechanism. We demonstrate the effectiveness of the P-DAT architecture in classic top tagging and quark–gluon discrimination tasks, achieving competitive performance compared to other benchmark strategies.

An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images

Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention. However, existing methods struggle to achieve a good balance between detection accuracy and efficiency. To address this issue, we propose a novel method for efficient PV cell defect detection. Firstly, we utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to improve EL image contrast, making defect features become more distinguishable. Secondly, we propose a lightweight defect detector using EfficientNet-B0 as its backbone. Moreover, we design a graph channel attention module (GCAM) to improve CNN’s limitation in modeling global information. It executes graph channel reasoning to generate enriched feature representation beyond the local receptive field, which is beneficial for distinguishing PV cell defects with similar local details. Next, we utilize focal loss to train the detector, enhancing its ability to detect challenging defects. Lastly, the proposed method is evaluated on the PVEL dataset and it achieved an accuracy of 97.81%, precision of 97.70%, recall of 97.59%, F1-score of 97.64%, and MCC of 97.32%, demonstrating our method is effective and outperforms state-of-the-art methods across various metrics.

Health state identification of circulating seawater pump-unit in nuclear power plant based on multi-virtual vibration source fusion in the presence of strong data imbalance

The circulating seawater pump-unit (CRF pump-unit) is a crucial equipment that provides cooling water for the secondary circuit of a nuclear power plant (NPP). Its long-term, fault-free operation ensures nuclear power safety and economy. In this study, considering the limited fault-state samples in the historical database and the random fault locations in NPP equipment, a health state identification framework of the CRF pump-unit is proposed, which combines multiple sources of network input and a feature fusion network. The proposed kernelled independent component analysis with reference (KICA-R) is utilized with reference signals to separate virtual sources from multiple raw acceleration signals. It enhances the fault characteristic frequencies related to several subsystems of a complex mechatronic system in the training samples of the network. Based on the newly designed multi-source channel attention module (MS-CAM) that can adaptively allocate fault-sensitive weight coefficients, a health state identification network incorporates the enhanced input features through the virtual sources obtained from KICA-R. By conducting the benchmark analysis on two public datasets and various types of fault simulation tests on the CRF pump-unit simulator, training sets with different levels of data imbalance are constructed, and the optimal network parameters are determined. The comparisons against cICA validate the ability of KICA-R in virtual source separation, and the strong performance of the MS-CAM-based network in identifying different health states of the CRF pump-unit is further demonstrated through detailed comparative analysis.

Research on water extraction from high resolution remote sensing images based on deep learning

Introduction: Monitoring surface water through the extraction of water bodies from high-resolution remote sensing images is of significant importance. With the advancements in deep learning, deep neural networks have been increasingly applied to high-resolution remote sensing image segmentation. However, conventional convolutional models face challenges in water body extraction, including issues like unclear water boundaries and a high number of training parameters.Methods: In this study, we employed the DeeplabV3+ network for water body extraction in high-resolution remote sensing images. However, the traditional DeeplabV3+ network exhibited limitations in segmentation accuracy for high-resolution remote sensing images and incurred high training costs due to a large number of parameters. To address these issues, we made several improvements to the traditional DeeplabV3+ network: Replaced the backbone network with MobileNetV2. Added a Channel Attention (CA) module to the MobileNetV2 feature extraction network. Introduced an Atrous Spatial Pyramid Pooling (ASPP) module. Implemented Focal loss for balanced loss computation.Results: Our proposed method yielded significant enhancements. It not only improved the segmentation accuracy of water bodies in high-resolution remote sensing images but also effectively reduced the number of network parameters and training time. Experimental results on the Water dataset demonstrated superior performance compared to other networks: Outperformed the U-Net network by 3.06% in terms of mean Intersection over Union (mIoU). Outperformed the MACU-Net network by 1.03%. Outperformed the traditional DeeplabV3+ network by 2.05%. The proposed method surpassed not only the traditional DeeplabV3+ but also U-Net, PSP-Net, and MACU-Net networks.Discussion: These results highlight the effectiveness of our modified DeeplabV3+ network with MobileNetV2 backbone, CA module, ASPP module, and Focal loss for water body extraction in high-resolution remote sensing images. The reduction in training time and parameters makes our approach a promising solution for accurate and efficient water body segmentation in remote sensing applications.

A Machine Anomalous Sound Detection Method Using the lMS Spectrogram and ES-MobileNetV3 Network

Unsupervised anomalous sound detection by machines holds significant importance within the realm of industrial automation. Currently, the task of machine-based anomalous sound detection in complex industrial settings is faced with issues such as the challenge of extracting acoustic feature information and an insufficient feature extraction capability within the detection network. To address these challenges, this study proposes a machine anomalous sound detection method using the lMS spectrogram and ES-MobileNetV3 network. Firstly, the log-Mel spectrogram feature and the SincNet spectrogram feature are extracted from the raw wave, and the new lMS spectrogram is formed after fusion, serving as network input features. Subsequently, based on the MobileNetV3 network, an improved detection network, ES-MobileNetV3, is proposed in this paper. This network incorporates the Efficient Channel Attention module and the SoftPool method, which collectively reduces the loss of feature information and enhances the feature extraction capability of the detection network. Finally, experiments are conducted on the dataset provided by DCASE 2020 Task 2. Our proposed method attained an averaged area under the receiver operating characteristic curve (AUC) of 96.67% and an averaged partial AUC (pAUC) of 92.38%, demonstrating superior detection performance compared to other advanced methods.

Patch SVDD(support vector data description)-based channel attention embedding and improvement of classifier

Some anomaly detection methods are based on CNN to fuse spatial and channel-wise information together within local receptive fields. However, the correlation between feature channels has not been fully utilized. Channel attention has been shown to model the interdependence between convolution feature channels and improve network representation. It is possible to introduce channel attention into anomaly detection. We attempt to directly embed the SE(Squeeze and Excitation) module into the convolutional layer but reduced anomaly detection performance. Therefore, we propose a lightweight channel attention module C-SE(Current Squeeze and Excitation) suitable for anomaly detection. C-SE module not only improves the representation ability of depth convolutional neural network but also has a significant effect on texture anomaly detection. C-SE module body is constructed by average pooling and maximum pooling branches, which ensure that local salient features of the image are not lost. Then reduce the negative impact of feature calibration through a long connection. In addition, the improvement of classifier plays an important role. Experimental results have shown that the proposed method outperforms the Patch SVDD methods by 3% in image-level AUROC and 0.7% in pixel-level AUROC on the MVTec benchmark. The higher AUROC score and the faster rate of convergence prove the effectiveness of the method.

Exploring quantitative assessment of cybersickness in virtual reality using EEG signals and a CNN-ECA-LSTM network

Cybersickness in the virtual reality (VR) environment presents a significant challenge for user experience. Accurate evaluation methods are essential to mitigate or prevent cybersickness for the users. Compared to traditional subjective user studies such as questionnaires, electroencephalography (EEG) has the advantage of high temporal resolution, enabling more precise real-time evaluation. In this paper, we propose a novel hybrid deep learning framework that integrates a convolutional neural network (CNN), efficient channel attention (ECA) module, and long short-term memory (LSTM) to evaluate VR cybersickness using EEG signals quantitatively. A novel experimental paradigm is developed to induce cybersickness through virtual head rotation. A total of thirty-six subjects were recruited for the experiment, and both EEG signals and subjective questionnaire data were collected accordingly to build a cybersickness-EEG dataset. The proposed framework enables a meaningful evaluation of cybersickness, and the experiment demonstrates the framework’s effective ability to decode the correlation between EEG and cybersickness. The relevance between EEG signals and model predictions is also investigated to demonstrate the interpretability and effectiveness of the proposed framework. Our findings reveal a robust correlation between cybersickness and heightened activation intensity in the occipital and temporal areas.

A LiDAR Multi-Object Detection Algorithm for Autonomous Driving

Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and categories to provide reliable operational decisions for achieving autonomous driving. To improve the detection and localization accuracy of multi-object targets such as surrounding vehicles and pedestrians in autonomous driving scenarios, based on PointPillars fast object detection network, a three-dimensional object detection algorithm based on the channel attention mechanism, ECA Modules-PointPillars, is proposed. Firstly, the improved algorithm uses point cloud columnarization features to convert a three-dimensional point cloud into a two-dimensional pseudo-image. Then, combining the 2D backbone network for feature extraction with the Efficient Channel Attention (ECA) modules to achieve the enhancement of the positional feature information in the pseudo-image and the weakening of the irrelevant feature information such as background noise. Finally, the single-shot multibox detector (SSD) algorithm was used to complete the 3D object detection task. The experimental results show that the improved algorithm improves the mAP by 3.84% and 4.04% in BEV mode and 3D mode, respectively, compared to PointPillars, which improves the mAP by 4.64% and 5.89% in BEV mode and 3D mode, respectively, compared to F-PointNet, improves the mAP by 11.78% and 14.19% in BEV mode and 3D mode, respectively, compared to VoxelNet, and improves the mAP by 9.47% and 6.55% in BEV mode and 3D mode, respectively, compared to SECOND, demonstrating the effectiveness and reliability of the improved algorithms in autonomous driving scenarios.