Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate a...Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion loss.First,the backbone network is built on CSPDarkNet53.Then a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the model.Finally,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects.Sufficient experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection speed.The mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset.展开更多
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr...Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.展开更多
BACKGROUND The frequency and content of follow-up strategies remain controversial for colorectal cancer(CRC),and scheduled follow-ups have limited value.AIM To compare intensive and conventional follow-up strategies f...BACKGROUND The frequency and content of follow-up strategies remain controversial for colorectal cancer(CRC),and scheduled follow-ups have limited value.AIM To compare intensive and conventional follow-up strategies for the prognosis of non-metastatic CRC treated with curative intent using a meta-analysis.METHODS PubMed,Embase,and the Cochrane Library databases were systematically searched for potentially eligible randomized controlled trials(RCTs)from inception until April 2023.The Cochrane risk of bias was used to assess the methodological quality of the included studies.The hazard ratio,relative risk,and 95%confidence interval were used to calculate survival and categorical data,and pooled analyses were performed using the random-effects model.Additional exploratory analyses were performed for sensitivity,subgroups,and publication bias.RESULTS Eighteen RCTs involving 8533 patients with CRC were selected for the final analysis.Intensive follow-up may be superior to conventional follow-up in improving overall survival,but this difference was not statistically significant.Moreover,intensive follow-up was associated with an increased incidence of salvage surgery compared to conventional follow-up.In addition,there was no significant difference in the risk of recurrence between intensive and conventional follow-up strategies,whereas intensive follow-up was associated with a reduced risk of interval recurrence compared to conventional follow-up.Finally,the effects of intensive and conventional follow-up strategies differed when stratified by tumor location and follow-up duration.CONCLUSION Intensive follow-up may have a beneficial effect on the overall survival of patients with non-metastatic CRC treated with curative intent.展开更多
基金Doctoral Talent Training Project of Chongqing University of Posts and Telecommunications,Grant/Award Number:BYJS202007Natural Science Foundation of Chongqing,Grant/Award Number:cstc2021jcyj-msxmX0941+1 种基金National Natural Science Foundation of China,Grant/Award Number:62176034Scientific and Technological Research Program of Chongqing Municipal Education Commission,Grant/Award Number:KJQN202101901。
文摘Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion loss.First,the backbone network is built on CSPDarkNet53.Then a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the model.Finally,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects.Sufficient experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection speed.The mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset.
基金supported by the National Natural Science Foundation of China(No.62176034)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202300604)the Natural Science Foundation of Chongqing(Nos.cstc2021jcyj-msxmX0518,2023NSCQ-MSX1781).
文摘Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.
文摘BACKGROUND The frequency and content of follow-up strategies remain controversial for colorectal cancer(CRC),and scheduled follow-ups have limited value.AIM To compare intensive and conventional follow-up strategies for the prognosis of non-metastatic CRC treated with curative intent using a meta-analysis.METHODS PubMed,Embase,and the Cochrane Library databases were systematically searched for potentially eligible randomized controlled trials(RCTs)from inception until April 2023.The Cochrane risk of bias was used to assess the methodological quality of the included studies.The hazard ratio,relative risk,and 95%confidence interval were used to calculate survival and categorical data,and pooled analyses were performed using the random-effects model.Additional exploratory analyses were performed for sensitivity,subgroups,and publication bias.RESULTS Eighteen RCTs involving 8533 patients with CRC were selected for the final analysis.Intensive follow-up may be superior to conventional follow-up in improving overall survival,but this difference was not statistically significant.Moreover,intensive follow-up was associated with an increased incidence of salvage surgery compared to conventional follow-up.In addition,there was no significant difference in the risk of recurrence between intensive and conventional follow-up strategies,whereas intensive follow-up was associated with a reduced risk of interval recurrence compared to conventional follow-up.Finally,the effects of intensive and conventional follow-up strategies differed when stratified by tumor location and follow-up duration.CONCLUSION Intensive follow-up may have a beneficial effect on the overall survival of patients with non-metastatic CRC treated with curative intent.
