To improve the accuracy and efficiency of coal-rock interface recognition,this study proposes a model built on the real-time detection algorithm,you only look once(YOLO),and the lightweight bilateral segmentation netw...To improve the accuracy and efficiency of coal-rock interface recognition,this study proposes a model built on the real-time detection algorithm,you only look once(YOLO),and the lightweight bilateral segmentation network.Simultaneously,the regional similarity transformation function and dragonfly algorithm are introduced to enhance the quality of coal-rock images.The comparison with three other models demonstrates the superior edge inference performance of the proposed model,achieving a mean Average Precision(mAP)of 90.2 at the Intersection over Union(IoU)threshold of 0.50(mAP50)and 81.4 across a range of IoU thresholds from 0.50 to 0.95(mAP[50,95]).Furthermore,to maintain high accuracy and real-time recognition capabilities,the proposed model is optimized using the open visual inference and neural network optimization toolkit,resulting in a 144.97%increase in the mean frames per second.Experimental results on four actual coal faces confirm the efficacy of the proposed model,showing a better balance between accuracy and efficiency in coal-rock image recognition,which supports further advancements in coal mining intelligence.展开更多
Human vision depends heavily on retinal tissue.The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all.Additionally,the diagnosis is susceptible to inaccura...Human vision depends heavily on retinal tissue.The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all.Additionally,the diagnosis is susceptible to inaccuracies when a large dataset is involved.Therefore,a fully automated transfer learning approach for diagnosing diabetic retinopathy(DR)is suggested to minimize human intervention while maintaining high classification accuracy.To address this issue,we proposed a transfer learning-based trilateral attention network(TaNet)for the classification.To boost the visual quality of the DR pictures,a contrast constrained adaptive histogram equalization approach is applied.The pre-processed pictures are then segmented using a bilateral segmentation network(BiSeNet).The BiSeNet segmented the optic disc and blood vessels individually.After the completion of segmentation,the features are extracted.Feature extraction is based on the wavelet scattering transformation approach.The results of many trials were evaluated against the Messidor-2,EYEPACS,and APTOS 2019 datasets.The proposed model was created using a refined pre-trained technique and transfer learning methodology.Finally,the suggested framework was tested using efficiency assessment methods,and the classification rate was recorded as having above 98%sensitivity,specificity,precision,and accuracy.The proposed approach yields greater performance and shows enhancement towards the existing approach.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.U21A20153 and 52074258)the Key Research and Development Project of Hubei Province,China(Grant No.2021BCA133)+1 种基金the Outstanding Youth Fund Program of the Natural Science Foundation of Hubei Province,China(Grant No.2022CFA084)the Wuhan Knowledge Innovation Supporting project(Grant No.2022010801010162).
文摘To improve the accuracy and efficiency of coal-rock interface recognition,this study proposes a model built on the real-time detection algorithm,you only look once(YOLO),and the lightweight bilateral segmentation network.Simultaneously,the regional similarity transformation function and dragonfly algorithm are introduced to enhance the quality of coal-rock images.The comparison with three other models demonstrates the superior edge inference performance of the proposed model,achieving a mean Average Precision(mAP)of 90.2 at the Intersection over Union(IoU)threshold of 0.50(mAP50)and 81.4 across a range of IoU thresholds from 0.50 to 0.95(mAP[50,95]).Furthermore,to maintain high accuracy and real-time recognition capabilities,the proposed model is optimized using the open visual inference and neural network optimization toolkit,resulting in a 144.97%increase in the mean frames per second.Experimental results on four actual coal faces confirm the efficacy of the proposed model,showing a better balance between accuracy and efficiency in coal-rock image recognition,which supports further advancements in coal mining intelligence.
文摘Human vision depends heavily on retinal tissue.The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all.Additionally,the diagnosis is susceptible to inaccuracies when a large dataset is involved.Therefore,a fully automated transfer learning approach for diagnosing diabetic retinopathy(DR)is suggested to minimize human intervention while maintaining high classification accuracy.To address this issue,we proposed a transfer learning-based trilateral attention network(TaNet)for the classification.To boost the visual quality of the DR pictures,a contrast constrained adaptive histogram equalization approach is applied.The pre-processed pictures are then segmented using a bilateral segmentation network(BiSeNet).The BiSeNet segmented the optic disc and blood vessels individually.After the completion of segmentation,the features are extracted.Feature extraction is based on the wavelet scattering transformation approach.The results of many trials were evaluated against the Messidor-2,EYEPACS,and APTOS 2019 datasets.The proposed model was created using a refined pre-trained technique and transfer learning methodology.Finally,the suggested framework was tested using efficiency assessment methods,and the classification rate was recorded as having above 98%sensitivity,specificity,precision,and accuracy.The proposed approach yields greater performance and shows enhancement towards the existing approach.
