The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency.The proposed method is based on Unmanned Aerial Vehicle(UAV)and computer...The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency.The proposed method is based on Unmanned Aerial Vehicle(UAV)and computer vision technology.First,a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions.Second,the average crack detection precisions of different methods including the Single Shot MultiBox Detector,You Only Look Once v3,You Only Look Once v4,Faster Regional Convolutional Neural Network(R-CNN)and Mask R-CNN methods were compared.Then,the Mask R-CNN method with the best performance and average precision of 0.34 was selected.Finally,based on the characteristics of cracks,the utilization ratio of Mask R-CNN to the underlying features was improved so that the average precision of 0.9 was achieved.It was found that the positioning accuracy and mask coverage rate of the proposed Mask R-CNN method are greatly improved.Also,it will be shown that using UAV is safer than manual detection because manual parameter setting is not required.In addition,the proposed detection method is expected to greatly reduce the cost and risk of manual detection of building exterior wall cracks and realize the efficient identification and accurate labeling of building exterior wall cracks.展开更多
Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow ...Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition®. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.展开更多
为了提高羊只计数的准确性和实用性,本文结合计算机视觉技术,提出了一种基于Mask R-CNN轻量级羊只计数算法。针对数据集的制作,前往内蒙古呼和浩特白塔村的养殖户进行数据采集,制作了羊只图像分割数据集。在对模型的轻量化部分,首先,将...为了提高羊只计数的准确性和实用性,本文结合计算机视觉技术,提出了一种基于Mask R-CNN轻量级羊只计数算法。针对数据集的制作,前往内蒙古呼和浩特白塔村的养殖户进行数据采集,制作了羊只图像分割数据集。在对模型的轻量化部分,首先,将特征提取网络的部分替换为 Inverted Residual模块并加入SE注意力机制,在保证模型分割准确度不下降的情况下降低模型的规模。其次,使用空间卷积池化金字塔ASPP进一步对模型的特征融合部分进行优化,最后利用改进后Mask R-CNN生成的掩膜进行计数。结果表明:改进后的Mask R-CNN-InvertedResidual-SE-ASPP羊只计数模型,计数准确率达到96.27%,较基准模型参数量减少38.46%,计算量减小26.14%,体积减小34.52%,单帧推理速度提升22.12%。说明,改进后的Mask R-CNN更适合实际应用中的高效羊只计数。To enhance the accuracy and practicality of sheep counting, this paper proposes a lightweight sheep counting algorithm based on Mask R-CNN combined with computer vision technology. For data set creation, we collected sheep images from local farms in Baita Village, Hohhot, Inner Mongolia, to create a sheep image segmentation dataset. To lighten the model, we replaced parts of the feature extraction network with Inverted Residual modules and incorporated Squeeze-and-Excitation (SE) attention mechanisms to maintain segmentation accuracy while reducing the model’s size. Additionally, we optimized the model’s feature fusion part using the Atrous Spatial Pyramid Pooling (ASPP) structure. Finally, we used the masks generated by the improved Mask R-CNN for counting. The results show that the improved Mask R-CNN-Inverted Residual-SE-ASPP sheep counting model achieved an accuracy rate of 96.27%, reduced the number of parameters by 38.46%, decreased computational complexity by 26.14%, reduced the model size by 34.52%, and increased single-frame inference speed by 22.12%. This indicates that the improved Mask R-CNN is more suitable for efficient sheep counting in practical applications.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 51408063,author W.C,http://www.nsfc.gov.cn/in part by the Outstanding Youth Scholars of the Department of Hunan Provincial under Grant 20B031,author W.C,http://kxjsc.gov.hnedu.cn/.
文摘The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency.The proposed method is based on Unmanned Aerial Vehicle(UAV)and computer vision technology.First,a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions.Second,the average crack detection precisions of different methods including the Single Shot MultiBox Detector,You Only Look Once v3,You Only Look Once v4,Faster Regional Convolutional Neural Network(R-CNN)and Mask R-CNN methods were compared.Then,the Mask R-CNN method with the best performance and average precision of 0.34 was selected.Finally,based on the characteristics of cracks,the utilization ratio of Mask R-CNN to the underlying features was improved so that the average precision of 0.9 was achieved.It was found that the positioning accuracy and mask coverage rate of the proposed Mask R-CNN method are greatly improved.Also,it will be shown that using UAV is safer than manual detection because manual parameter setting is not required.In addition,the proposed detection method is expected to greatly reduce the cost and risk of manual detection of building exterior wall cracks and realize the efficient identification and accurate labeling of building exterior wall cracks.
文摘Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition®. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.
文摘为了提高羊只计数的准确性和实用性,本文结合计算机视觉技术,提出了一种基于Mask R-CNN轻量级羊只计数算法。针对数据集的制作,前往内蒙古呼和浩特白塔村的养殖户进行数据采集,制作了羊只图像分割数据集。在对模型的轻量化部分,首先,将特征提取网络的部分替换为 Inverted Residual模块并加入SE注意力机制,在保证模型分割准确度不下降的情况下降低模型的规模。其次,使用空间卷积池化金字塔ASPP进一步对模型的特征融合部分进行优化,最后利用改进后Mask R-CNN生成的掩膜进行计数。结果表明:改进后的Mask R-CNN-InvertedResidual-SE-ASPP羊只计数模型,计数准确率达到96.27%,较基准模型参数量减少38.46%,计算量减小26.14%,体积减小34.52%,单帧推理速度提升22.12%。说明,改进后的Mask R-CNN更适合实际应用中的高效羊只计数。To enhance the accuracy and practicality of sheep counting, this paper proposes a lightweight sheep counting algorithm based on Mask R-CNN combined with computer vision technology. For data set creation, we collected sheep images from local farms in Baita Village, Hohhot, Inner Mongolia, to create a sheep image segmentation dataset. To lighten the model, we replaced parts of the feature extraction network with Inverted Residual modules and incorporated Squeeze-and-Excitation (SE) attention mechanisms to maintain segmentation accuracy while reducing the model’s size. Additionally, we optimized the model’s feature fusion part using the Atrous Spatial Pyramid Pooling (ASPP) structure. Finally, we used the masks generated by the improved Mask R-CNN for counting. The results show that the improved Mask R-CNN-Inverted Residual-SE-ASPP sheep counting model achieved an accuracy rate of 96.27%, reduced the number of parameters by 38.46%, decreased computational complexity by 26.14%, reduced the model size by 34.52%, and increased single-frame inference speed by 22.12%. This indicates that the improved Mask R-CNN is more suitable for efficient sheep counting in practical applications.