Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.

基于DeeplabV3+网络的轻量化语义分割算法

针对传统语义分割模型参数量大、计算速度慢且效率不高等问题,改进一种基于DeeplabV3+网络的轻量化语义分割模型Faster-DeeplabV3+。Faster-DeeplabV3+模型采用轻量级MobilenetV2代替Xception作为主干特征提取网络,大幅减少参数量,提高计算速度;引入深度可分离卷积(deep separable convolution, DSC)与空洞空间金字塔(atrous spatia pyramid pooling, ASPP)中的膨胀卷积设计成新的深度可分离膨胀卷积(depthwise separable dilated convolution, DSD-Conv),即组成深度可分离空洞空间金字塔模块(DP-ASPP),扩大感受野的同时减少原本卷积参数量,提高运算速度;加入改进的双注意力机制模块分别对编码区生成的低级特征图和高级特征图进行处理,增强网络对不同维度特征信息提取的敏感性和准确性;融合使用交叉熵和Dice Loss两种损失函数,为模型提供更全面、更多样的优化。改进模型在PASCAL VOC 2012数据集上进行测试。实验结果表明:平均交并比由76.57%提升至79.07%,分割准确度由91.2%提升至94.3%。改进模型的网络参数量(params)减少了3.86×10~6,浮点计算量(GFLOPs)减少了117.98 G。因此,Faster-DeeplabV3+算法在大幅降低参数量、提高运算速度的同时保持较高语义分割效果。

SepFE:Separable Fusion Enhanced Network for Retinal Vessel Segmentation

The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable effectiveness.Here,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular architectures.In view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional layer.The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for themodel.To ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the encoder.Then,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct fusion.Finally,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is obtained.The superiority of SepFE is further demonstrated through comparisons with other advanced methods.

面向电台通信的CLU-Net语音增强网络

为了消除电台系统中的环境噪声和信道噪声对语音通信质量的不利影响,提升电台语音通信的质量,提出了一种基于联合通道注意力与长短时记忆网络(Long Short Term Memory,LSTM)的深度可分离U形网络CLU-Net(Channel Attention and LSTM-based U-Net)。该网络采用深度可分离卷积实现低复杂度的特征提取,联合利用注意力机制和LSTM同时关注语音通道特征和长时上下文联系,在参数量较少的情况下实现对干净语音特征的关注。在公开与实测数据集上进行多组对比实验,仿真结果表明,所提方法在VoiceBank-DEMAND数据集上的PESQ和STOI等指标得分优于同类语音增强模型。实测实验结果表明,所提CLU-Net增强框架能够有效抑制环境噪声与信道噪声,在低信噪比条件下的增强性能优于其他同类型的增强网络。

Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping nuclei.To this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation accuracy.Additionally,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain normalization.The proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ dataset.These findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.

基于Dual Dense U-Net的云南壁画破损区域预测

壁画破损区域预测是壁画虚拟修复工作的重要环节,针对现有方法在预测云南少数民族壁画破损区域时容易出现破损区域预测不全、对纹理复杂区域的破损边界预测不准确等问题,提出了一种基于U-Net改进的Dual Dense U-Net分割模型,通过增强破损区域位置特征和纹理特征,获取更多的判别信息,以提高破损掩膜预测的准确度。为使模型能更有效地学习壁画特征,建立了一个包含5 000张云南少数民族壁画图像的分割数据集。Dual Dense U-Net模型利用融合模块去对壁画图像进行多尺度融合,减少壁画图像在前馈过程中的局部纹理信息和空间位置信息损失。首先,利用U-Net结构对输入的壁画图像进行信息提取,融合模块有多个深度可分离卷积,能够提高融合模块效率以及分割精度;其次,融合模块连接两个U-Net,进一步加强浅层特征与深层特征间的联系。实验结果表明,该模型在IoU与Dice评价指标较UNet++提高了3个百分点,模型预测得到的破损区域能显著改善壁画修复网络的修复效果,验证了该模型在壁画破损区域预测领域的有效性。

Inner Cascaded U^(2)-Net:An Improvement to Plain Cascaded U-Net

Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction.U-Net has been the baseline model since the very beginning due to a symmetricalU-structure for better feature extraction and fusing and suitable for small datasets.To enhance the segmentation performance of U-Net,cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine.However,the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the latter one.In this article,we devise novel Inner Cascaded U-Net and Inner Cascaded U^(2)-Net as improvements to plain cascaded U-Net for medical image segmentation.The proposed Inner Cascaded U-Net adds inner nested connections between two U-Nets to share more contextual information.To further boost segmentation performance,we propose Inner Cascaded U^(2)-Net,which applies residual U-block to capture more global contextual information from different scales.The proposed models can be trained from scratch in an end-to-end fashion and have been evaluated on Multimodal Brain Tumor Segmentation Challenge(BraTS)2013 and ISBI Liver Tumor Segmentation Challenge(LiTS)dataset in comparison to related U-Net,cascaded U-Net,U-Net++,U^(2)-Net and state-of-the-art methods.Our experiments demonstrate that our proposed Inner Cascaded U-Net and Inner Cascaded U^(2)-Net achieve better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as get finer outline segmentation.

Image Semantic Segmentation for Autonomous Driving Based on Improved U-Net

Image semantic segmentation has become an essential part of autonomous driving.To further improve the generalization ability and the robustness of semantic segmentation algorithms,a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism(SE)and Depthwise Separable Convolution(DSC)is designed.Meanwhile,Adam-GC,an Adam optimization algorithm based on Gradient Compression(GC),is proposed to improve the training speed,segmentation accuracy,generalization ability and stability of the algorithm network.To verify and compare the effectiveness of the algorithm network proposed in this paper,the trained networkmodel is used for experimental verification and comparative test on the Cityscapes semantic segmentation dataset.The validation and comparison results show that the overall segmentation results of the algorithmnetwork can achieve 78.02%MIoU on Cityscapes validation set,which is better than the basic algorithm network and the other latest semantic segmentation algorithms network.Besides meeting the stability and accuracy requirements,it has a particular significance for the development of image semantic segmentation.

基于改进SegNet的鸡只检测算法

为实现智能化检测出鸡场中死亡鸡只,提出一种基于改进语义分割模型AT-SegNet的鸡只检测算法。基于对称编码解码结构SegNet,利用空洞卷积在解码前聚合不同感受野的上下文信息,设计一种三尺度注意力级联融合模块,以并联方式嵌入编、解码器间,丰富解码器信息。利用多层深度可分离卷积替代标准卷积,提取深层次语义信息,减少计算量提高实时性。将鸡群图像分割结果交并比与阈值对比判别鸡只状态。实验结果表明,改进的AT-SegNet较原算法的检测精度提高了25.17%,能够在复杂鸡群环境中准确、高效地发现死亡鸡只。

裂缝小目标缺陷的轻量化检测方法

及时且准确捕获井壁出现的微小裂缝,对于井筒安全意义重大.轻量化检测模型是推动井壁裂缝自动检测的关键,打破现有方法聚焦于提取深层语义信息的局限,重视浅层特征表征的几何结构信息的应用,针对井壁裂缝提出轻量化检测模型E-YOLOv5s.首先融合普通卷积、深度可分离卷积和ECA注意力机制设计轻量化卷积模块ECAConv,再引入跳跃链接构建特征综合提取单元E-C3,得到骨干网络ECSP-Darknet53,它负责显著降低网络参数,同时增强对裂缝深层特征的提取能力.然后设计特征融合模块ECACSP,利用多组ECAConv和ECACSP模块组建细颈部特征融合模块E-Neck,旨在充分融合裂缝小目标的几何信息和表征裂缝开裂程度的语义信息,同时加快网络推理速度.实验表明,E-YOLOv5s在自制井壁数据集上的检测精度相较YOLOv5s提升了4.0%,同时模型参数量和GFLOPs分别降低了44.9%、43.7%.E-YOLOv5s有助于推动井壁裂缝自动检测的应用.

基于轻量化YOLOv4的死淘鸡目标检测算法

针对目前死淘鸡目标检测研究较少,高精度检测算法体积大难以部署至移动式设备等问题,提出一种基于YOLOv4的轻量化死淘鸡目标检测算法。采集大规模蛋鸡养殖工厂笼中死淘鸡图片,建立目标检测数据集;在算法中引入MobileNetv3主干提取网络与深度可分离卷积来降低模型体积;并在最大池化层前添加自注意力机制模块,增强算法对全局语义信息的捕获。在自建数据集中的试验结果表明,改进算法在死淘鸡目标检测任务中有更高的准确度,其mAP值与召回率分别达到97.74%和98.15%,模型大小缩小至原算法的1/5,在GPU加速下帧数达到77帧/s,检测速度提高1倍,能够满足嵌入式部署需求。

Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic data.CNN based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters selection.However,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale features.To overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data reconstruction.We firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data.Moreover,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial dimensions.Finally,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness.The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

基于深度学习的机织物起毛起球客观评级分析

为了有效克服目前人工检测速度慢、误差大、主观性强的问题,更快速、准确、客观地对机织物起毛起球样本评级,提出了一种多尺度特征融合的Wide-SqueezeNet网络。首先制作了两种成分不同的机织物起毛起球数据集。根据机织物起毛起球图像中毛球形状大小不一以及分布不均的特点,在网络中改进Fire模块,其中增加了短连接来解决训练中梯度发散等问题,在短连接中使用两个3×3小卷积核来减少计算量并且获取不同尺度的特征图信息,增强网络的特征提取能力来提高准确率;其次为了减少整体的计算量,在网络预处理时将图像统一到224×224大小,并且将网络中普通卷积替换为深度可分离卷积。结果表明,通过使用多尺度特征融合和深度可分离卷积来改进网络,机织物起毛起球的客观评级准确率可以达到99.333%。相比于基础网络SqueezeNe、Resnet、MobileNet、DenseNet、ShuffleNet,该方法的提升分别为2.220%、1.777%、2.666%、1.333%和2.220%。与人工检测需要几分钟到十几分钟不等相比,该网络只需要0.072 s即可检测一幅图像,检测速度大大提高。

5G技术下WebAR跟踪注册技术分析与研究

基于浏览器端的WebAR与以应用程序为载体的AR相比,具有体积小、易传播和跨平台等优势。在WebAR的具体应用中跟踪注册的效果决定了系统性能和用户体验。跟踪注册的目标是较准确地计算出相关设备的位置和姿态信息,通过计算得出要添加的虚拟模型在实际场景中的位置,从而实现虚实融合。将WebAR在用户体验和传播中的优势、5G技术下WebAR在具体应用中的特征和WebAR中常见的跟踪注册技术进行分析的基础上,探讨5G技术下轻量级卷积神经网络的WebAR跟踪注册技术研究。

基于深度可分离卷积神经网络的端到端高效车牌识别方法

车牌识别易受到天气或光照条件等不可预见干扰的影响。因此,文章提出了一种无需分割的车牌识别框架,该网络模型结合了先进的深度学习方法和设计思想,采用了深度可分离卷积来极大地降低计算量。与之前相比,它以更低的计算要求实现了更高的识别精度。在两个不同的数据集上对所提方法的有效性进行了评估,并获得了超过99%识别准确率和70以上的帧率,该方法稳健高效,值得推广。

基于多域信息融合与深度分离卷积的轴承故障诊断网络模型

针对传统卷积神经网络(CNN)对滚动轴承振动信号的故障识别准确率不高这一问题,提出了一种基于多域信息融合结合深度分离卷积(MDIDSC)的轴承故障诊断方法。首先,利用自适应噪声的完全集合经验模态分解(CEEMDAN)算法对轴承振动信号进行了分解;然后,利用分解出的本征模态函数(IMF)的各个分量构建了多空间状态矩阵,并将该多空间状态矩阵输入该深度分离卷积模型中,进行了卷积训练;同时,在该深度分离卷积模型中添加了残差结构,对数据特征进行了复利用,并对卷积核进行了深度分离,解决了深度模型的网络退化问题;最后,提出了一种空间特征提取方法,对模型参数进行了修剪,采用一种自适应学习率退火方法进行了梯度优化,以避免模型陷入局部最优。研究结果表明:通过对多个轴承故障数据集进行对比分析可知,MDIDSC在轴承故障诊断方面的准确率和稳定性明显优于其他方法,MDIDSC的最高测试准确率为100%,平均测试准确率为99.07%;同时,在测试集中的最大损失和平均损失分别为0.1345和0.0841;该结果表明MDIDSC在轴承故障诊断方面具有一定的优越性。

基于深度可分离卷积和残差注意力模块的车道线检测方法

针对全天候条件下道路车道线视觉检测技术存在的算法结构复杂、参数数量较多等问题,提出一种基于深度可分离卷积和残差注意力模块的车道线检测方法,建立了LPINet网络模型。利用深度可分离卷积减小输入图像尺寸,设计三种不同结构的瓶颈残差单元降低网络参数数量,引入ECANet注意力机制增加重要特征通道权重,提升车道线检测精度。在Tusimple数据集和GZUCDS自建数据集上的实验结果表明:在晴天场景下,LPINet网络车道线检测精度可达96.62%,且模型参数量降至1.64 MB,实现了轻量化设计;在雾天、雨天、夜晚和隧道复杂场景中进行了探索性研究,车道线检测精度达到93.86%,证明了方法的有效性。

基于改进V-Net的颅内出血病灶分割算法

针对颅内出血病灶分割不精确问题提出一种改进V-Net算法。用深度可分离卷积去替换普通卷积,加快模型训练速度。在编码器和解码器中分别加入通道注意力机制和混合注意力机制。通过引入SE模块和CBAM模块,强化原始网络的特征提取能力以及自适应调整特征图中不同通道之间的权重,提高模型的性能表现。对比实验结果表明,改进后的V-Net分割评价指标DSC达到0.732,比原始V-Net提升4.4%。

基于轻量级卷积网络的铣削粗糙度在机监测研究

传统机器学习类方法对光源类型、设备安装误差等因素较为敏感,需要反复调试与实验,难以实现规模化生产的自动检测.针对上述问题,提出了一种铣削粗糙度在机监测方法,有效提升了检测效率和准确性.首先,采用低感度参数设置的方向梯度直方图特征的候选框提取算子实现铣削工件的定位,并基于点匹配算法校正安装误差;然后,通过清晰度评价指标实现工业相机对焦过程优化;最后,构建了一种面向移动端实时计算的轻量级卷积神经网络模型,可对不同粗糙度工件表面纹理进行分类,并在立铣加工纹理数据集上进行了实验验证.实验结果表明:相比普通卷积神经网络,在模型复杂度相似的情况下,以乘、加运算次数为指标,提出模型推理所需运算量减少55%;代价敏感函数的引入能有效提升粗糙度识别模型对不平衡数据的稳定性;所提方法与传统机器学习方法相比,在检测帧率、图像分辨率相同的实验条件下,精准率、召回率分别提高了8%、21%.

基于多模态学习的乳腺癌生存预测研究

乳腺癌是最常见的癌症之一,基于患者的基因组学数据进行预后5年生存预测是乳腺癌研究中的常见任务。针对乳腺癌患者基因组学数据中存在嘈杂性、异质性、序列长以及正负样本不平衡等问题,提出基于多模态学习的乳腺癌预后5年生存预测模型MLBSP。利用单模态模块提取基因表达数据、基因突变累积数、单核苷酸变异以及基因拷贝数变异数4种模态数据的有效信息。在此基础上,为了减少单一模态数据异质性对全局特征造成的影响,将深度可分离卷积和多头自注意力机制作为多模态模块架构对数据进行特征融合,捕获患者多模态基因组数据的全局信息,并使用Focal Loss解决正负样本不平衡的问题,以指导预后5年生存预测。实验结果表明,MLBSP模型在乳腺癌患者真实数据集BRCACell、METABRIC、PanCancer Altas上的AUC分别达到91.18%、71.49%、77.37%,与XGBoost、随机森林等主流癌症生存预测方法相比,平均提升了17.69%、6.51%、10.24%。此外,通过通路分析发现一些生物标志物SLC8A3、TP 53等,进一步验证多模态研究的新颖性和有效性。