Review



adaptive fusion of faster r-cnn and shape from shading  (IEEE Access)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    IEEE Access adaptive fusion of faster r-cnn and shape from shading
    Adaptive Fusion Of Faster R Cnn And Shape From Shading, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/adaptive+fusion+of+faster+r+cnn+and+shape+from+shading/10__1109_slash_tdei__2024__3395230-310-24-29
    Average 90 stars, based on 1 article reviews
    adaptive fusion of faster r-cnn and shape from shading - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    other:

    Article Title: Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network
    Article Snippet: [35] C. Chaoxia, W. Shang, and F. Zhang, ‘‘Information-guided flame detection based on faster R-CNN,’’ IEEE Access, vol.



    Similar Products

    yolov5 faster r cnn hybrid  (Kaggle Inc)
    86
    Kaggle Inc yolov5 faster r cnn hybrid
    Yolov5 Faster R Cnn Hybrid, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/cnn+faster+hybrid+r+yolov5/pmc12756223-10-8-5
    Average 86 stars, based on 1 article reviews
    yolov5 faster r cnn hybrid - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier
    adaptive fusion of faster r-cnn and shape from shading  (IEEE Access)
    90
    IEEE Access adaptive fusion of faster r-cnn and shape from shading
    Adaptive Fusion Of Faster R Cnn And Shape From Shading, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/adaptive+fusion+of+faster+r+cnn+and+shape+from+shading/10__1109_slash_tdei__2024__3395230-310-24-29
    Average 90 stars, based on 1 article reviews
    adaptive fusion of faster r-cnn and shape from shading - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn  (MathWorks Inc)
    90
    MathWorks Inc faster r-cnn
    Parameter settings of different methods.
    Faster R Cnn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/pmc11548612-289-43-33
    Average 90 stars, based on 1 article reviews
    faster r-cnn - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn object detector  (MathWorks Inc)
    90
    MathWorks Inc faster r-cnn object detector
    Parameter settings of different methods.
    Faster R Cnn Object Detector, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/pmc11278429-82-13-17
    Average 90 stars, based on 1 article reviews
    faster r-cnn object detector - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn network  (MathWorks Inc)
    90
    MathWorks Inc faster r-cnn network
    Parameter settings of different methods.
    Faster R Cnn Network, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/pmc11278429-81-9-0
    Average 90 stars, based on 1 article reviews
    faster r-cnn network - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn image fusion algorithm  (Daheng New Epoch)
    90
    Daheng New Epoch faster r-cnn image fusion algorithm
    Parameter settings of different methods.
    Faster R Cnn Image Fusion Algorithm, supplied by Daheng New Epoch, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/faster+r+cnn+image+fusion+algorithm/10__56397_slash_fms__2024__02__10-148-26-1
    Average 90 stars, based on 1 article reviews
    faster r-cnn image fusion algorithm - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn  (Kuang Lung Shing)
    90
    Kuang Lung Shing faster r-cnn
    Parameter settings of different methods.
    Faster R Cnn, supplied by Kuang Lung Shing, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/faster+r+cnn/pmc10797131-9-13-2
    Average 90 stars, based on 1 article reviews
    faster r-cnn - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn framework  (Kuang Lung Shing)
    90
    Kuang Lung Shing faster r-cnn framework
    Chronological order of applications and developments of AI for facial fracture detection reviewed in this study.
    Faster R Cnn Framework, supplied by Kuang Lung Shing, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/faster+r+cnn+framework/pmc10797131-250-21-23
    Average 90 stars, based on 1 article reviews
    faster r-cnn framework - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    faster r-cnn + gan  (Kaggle Inc)
    90
    Kaggle Inc faster r-cnn + gan
    Summary of Eligible Cervical Cancer CAD Algorithms <xref ref-type= a , b , c , d " width="250" height="auto" />
    Faster R Cnn + Gan, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn/mask+r+cnn/pmc11975695-0-12-23
    Average 90 stars, based on 1 article reviews
    faster r-cnn + gan - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Parameter settings of different methods.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

    doi: 10.3390/s24217048

    Figure Lengend Snippet: Parameter settings of different methods.

    Article Snippet: This slow speed was primarily due to the small size of the koalas and hence the need to run multiple tiles through the detector for each image. shows the average processing times for MATLAB implementations of the AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, and Combined 2DCNN methods.

    Techniques:

    Evaluation curves for 11 comparative koala detection techniques (AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, Combined 2DCNN, and the MOBIVLS): ( a 1 – d 1 ) show the receiver operating characteristic ( R O C ) curves (TPR vs. FPR); ( a 2 – d 2 ) show the recall vs. (1-precision) curves; and ( a 3 – d 3 ) show the A U R O C and E E R percentages. The F P R range over which the A U R O C calculations were computed was (0– 10 − 4 ), while T P R range used was (0–1). The uppermost three rows of Figures show the results from datasets A–C, respectively, with the last row showing the overall (average) results. In all cases, the proposed MOBIVLS algorithm outperformed all of the other approaches tested.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

    doi: 10.3390/s24217048

    Figure Lengend Snippet: Evaluation curves for 11 comparative koala detection techniques (AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, Combined 2DCNN, and the MOBIVLS): ( a 1 – d 1 ) show the receiver operating characteristic ( R O C ) curves (TPR vs. FPR); ( a 2 – d 2 ) show the recall vs. (1-precision) curves; and ( a 3 – d 3 ) show the A U R O C and E E R percentages. The F P R range over which the A U R O C calculations were computed was (0– 10 − 4 ), while T P R range used was (0–1). The uppermost three rows of Figures show the results from datasets A–C, respectively, with the last row showing the overall (average) results. In all cases, the proposed MOBIVLS algorithm outperformed all of the other approaches tested.

    Article Snippet: This slow speed was primarily due to the small size of the koalas and hence the need to run multiple tiles through the detector for each image. shows the average processing times for MATLAB implementations of the AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, and Combined 2DCNN methods.

    Techniques:

    The overall results of the three datasets, which were computed by treating the datasets as a single entity. Several performance metrics were computed for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 56. The best result of each metric is highlighted by an underline and bold style. The second-best result is indicated by bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used. The column ‘Time’ represents the processing time in seconds per frame.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

    doi: 10.3390/s24217048

    Figure Lengend Snippet: The overall results of the three datasets, which were computed by treating the datasets as a single entity. Several performance metrics were computed for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 56. The best result of each metric is highlighted by an underline and bold style. The second-best result is indicated by bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used. The column ‘Time’ represents the processing time in seconds per frame.

    Article Snippet: This slow speed was primarily due to the small size of the koalas and hence the need to run multiple tiles through the detector for each image. shows the average processing times for MATLAB implementations of the AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, and Combined 2DCNN methods.

    Techniques:

    Results from Dataset A of several performance metrics for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 25. The best result of each metric is highlighted by an underline and bold style. The second-best result is written in bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

    doi: 10.3390/s24217048

    Figure Lengend Snippet: Results from Dataset A of several performance metrics for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 25. The best result of each metric is highlighted by an underline and bold style. The second-best result is written in bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used.

    Article Snippet: This slow speed was primarily due to the small size of the koalas and hence the need to run multiple tiles through the detector for each image. shows the average processing times for MATLAB implementations of the AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, and Combined 2DCNN methods.

    Techniques:

    Results from Dataset B of several performance metrics for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 25. The best result of each metric is highlighted by an underline and bold style. The second-best result is written in bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

    doi: 10.3390/s24217048

    Figure Lengend Snippet: Results from Dataset B of several performance metrics for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 25. The best result of each metric is highlighted by an underline and bold style. The second-best result is written in bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used.

    Article Snippet: This slow speed was primarily due to the small size of the koalas and hence the need to run multiple tiles through the detector for each image. shows the average processing times for MATLAB implementations of the AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, and Combined 2DCNN methods.

    Techniques:

    Results from Dataset C of several performance metrics for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 6. The best result of each metric is highlighted by an underline and bold style. The second-best result is written in bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

    doi: 10.3390/s24217048

    Figure Lengend Snippet: Results from Dataset C of several performance metrics for different object-detection techniques at (a) F P R of 10 − 6 and (b) 10 − 5 . The total number of unique koalas was 6. The best result of each metric is highlighted by an underline and bold style. The second-best result is written in bold style only. The proposed MOBIVLS algorithm performed better than all of the other techniques against all of the metrics used.

    Article Snippet: This slow speed was primarily due to the small size of the koalas and hence the need to run multiple tiles through the detector for each image. shows the average processing times for MATLAB implementations of the AAGD, IAAGD, HB-MLCM, ILCM, MLCM, MPCM, TMBM, Faster R-CNN, YOLOv2, and Combined 2DCNN methods.

    Techniques:

    Chronological order of applications and developments of AI for facial fracture detection reviewed in this study.

    Journal: Frontiers in Artificial Intelligence

    Article Title: A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

    doi: 10.3389/frai.2023.1278529

    Figure Lengend Snippet: Chronological order of applications and developments of AI for facial fracture detection reviewed in this study.

    Article Snippet: In the context of adult head injuries, an approach for the detection of skull fractures was developed, building upon the Faster R-CNN framework (Kuang et al., ).

    Techniques:

    Summary of Eligible Cervical Cancer CAD Algorithms <xref ref-type= a , b , c , d " width="100%" height="100%">

    Journal: Mayo Clinic Proceedings: Digital Health

    Article Title: A Review of Computer-Aided Diagnostic Algorithms for Cervical Neoplasia and an Assessment of Their Applicability to Female Genital Schistosomiasis

    doi: 10.1016/j.mcpdig.2023.04.007

    Figure Lengend Snippet: Summary of Eligible Cervical Cancer CAD Algorithms a , b , c , d

    Article Snippet: Elakkiya et al, 2021 , 99.49 , 96.92 , 98.55 , Faster R-CNN + GAN , 1,112 positives; 1,993 negatives , 80:20 , Kaggle challenge, clinical study.

    Techniques: