基于改进深度网络的车辆乘员数量检测研究

车辆乘员数量检测研究对推进HOV车道建设具有重要意义,本文以Faster RCNN网络模型为基础,结合多光谱红外系统获得的汽车驾驶室图像来展开研究.现有数据中因过曝、曝光不足等问题导致图像中目标特征差异大,网络检测的准确率不高,对此采用变形结构的卷积计算提高特征单元的感受野和目标边缘信息的表述能力,采用变形的ROI-P...     

基于改进Mask RCNN和SVR的无接触梭子蟹体质量预测研究

提出了一个改进Mask RCNN目标检测算法用以对养殖梭子蟹进行视觉特征测量. 通过在养殖区域采集梭子蟹图像, 用上位机识别梭子蟹旋转角度以及甲长和甲宽方向, 对输出的Mask进行模板修补, 提高模板内区域的置信度. 通过图像-实景对应关系换算梭子蟹的真实尺寸, 并估算其投影面积、甲宽与甲长, 结果准确率高于85%. 同时, 对视觉算法得到的梭子蟹尺寸特征与其体质量进行拟合, 引入k-means聚类, 实现双模型支持向量回归机(SVR)预测结构. 通过差分进化算法对SVR适应度函数进行寻优, 设计了随迭代次数、寻优效果同步变化的缩放因子, 以及适者更易生存策略的交叉概率因子, 以验证改进算法的寻优能力. 测试时, 对新传入的数据首先进行归一化处理, 然后判断所归属的聚类中心, 再传至相应的SVR模型进行预测. 测试结果相对误差小于18%.     

基于Mask RCNN的桥梁裂缝检测方法设计及研究北大核心CSCD

裂缝是桥梁道路上常见的一种病害,针对其检测准确率有待提高的问题,提出了基于Mask RCNN(region-based convolutional neural networks)的桥梁裂缝检测算法,设计了语义增强模块(semantic enhancement module,SEM),将该模块与特征金字塔网络(feature pyramid network,FPN)相结合,通过特征融合Add计算得到新的多尺度特征图feature maps。针对裂缝形态复杂多样存在识别困难的问题,将裂缝做了两类划分进行检测,并制定了两种策略进行对比实验。实验结果表明:该文中改进的方法可以得到更好的检测结果,检测准确率Accuracy可达99.8%,平均检测精度(mean average precision,mAP)提高了12.6%。     

基于可变形残差卷积与伸缩式特征金字塔算法的PCB缺陷检测

近年来深度学习技术在印刷电路板（Printed Circust Boord,PCB）缺陷检测上已获得快速进步,但现有算法针对PCB图像中多尺度高密度微小缺陷目标,如何精准高效地提取特征,提高检测精度及速度依然存在巨大挑战。提出了一种可变形残差卷积与伸缩式特征金字塔的PCB缺陷检测算法。在Faster RCNN的基础上,通过引入可变形残差卷积模块替换原始VGG16网络进行通道关系校准,提高算法对复杂缺陷目标特征的语义获取能力;利用一种伸缩式改进的特征金字塔NAS-FPN网络与原区域建议RPN网络融合,以改善算法对多尺度微小缺陷目标的识别能力;结合IoU Loss、Matrix NMS等tricks组合综合优化网络的检测精度及速度。通过实验,相比原始Faster RCNN,检测精度从90.08%提升到99.41%,检测速率从4.08 frame/s提升到6.47 frame/s。该方法能实现检测精度及速度双高的PCB缺陷检测,具有一定的实际意义。     

优化卷积网络及低分辨率热成像的夜间人车检测与识别

夜间环境下人车的检测与识别在自动驾驶,安防等领域具有重要意义.本文提出使用性价比较高的低分辨率红外热成像摄像机拍摄的图像来进行夜间的人车检测与识别,并根据图像独特的性质对Faster RCNN网络进行了优化.增加多通道卷积层来适应热成像图像的灰度特性.使用全局平均池化层来适应较少的图像及类别数量,增加批标准化层来防止加深加宽网络后可能出现的梯度消失或爆炸.使用在城市夜间环境中采集的2000张低分辨率热成像图像对网络进行训练与测试,平均准确识别率达到71.3％.相比于传统的检测手段,本组合方法在真实的场景中取得了较好的识别效果,同时提升了准确识别率,有效解决了夜间环境下人车检测与识别的问题,鲁棒性及应用价值较强.     

基于 Mask RCNN 的桥小脑角区脑膜瘤与听神经瘤分类定位研究

由于人体桥小脑角区的脑膜瘤与听神经瘤在影像学的表现以及发病位置极其相似,所以临床诊断极易发生误诊．针对此问题,本文应用掩膜区域卷积神经网络（Mask RCNN）对两类肿瘤进行分类定位研究．首先采集89名脑膜瘤与218名听神经瘤患者的T₁WI-SE序列的磁共振图像,对其进行预处理,再结合改进的特征金字塔网络（FPN）算法进行网络训练．本文对比了三种不同的Mask RCNN主干网络对两者分类定位的效果．结果表明,结合改进的FPN算法和ResNet101作为主干网络的Mask RCNN分类定位模型能够有效实现对两类肿瘤的分类定位,精确率为0.918 2、召回率为0.856 9、特异性为0.876 2、均值平均精度（mAP）为0.90．     

Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy

Data-driven fault diagnosis methods for rotating machinery have developed rapidly with the help of deep learning methods. However, traditional intelligent fault diagnosis methods still have some limitations in fault feature extraction and the latest object detection theory has not been applied in fault diagnosis. To this end, a fault diagnosis method based on a sparse short-term Fourier transform (SSTFT) and object detection theory is developed in this paper. First, a sparse constraint is introduced in time-frequency analysis to improve the time-frequency resolution of the model without cross-term interference and proximal gradient descent (PGD) is adopted to quickly and effectively optimize the model to obtain a high-quality time-frequency representation (TFR). Second, a fault diagnosis model based on a region-based convolutional neural network (RCNN) is built; the model can extract multiple regions that can characterize fault features from the TFR. This process avoids the interference of irrelevant vibration components and improves the interpretability of the fault diagnosis model. Finally, multicategory rolling bearing fault identification is realized. The effectiveness of the proposed method is validated by simulation signals and bearing experiments. The results indicate that the proposed method is more effective than existing methods.     

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.