Fruit Detection Based on Automatic Occlusion Prediction and Improved YOLOv5s -- 1 Introduction -- 2 Target Detection Based on Occlusion Information Automatic Judgment -- 2.1 Analysis of Occlusion Information for Target Detection -- 2.2 Automatically Generate Occlusion Information -- 3 Model and Improvement -- 3.1 Embedded Attention Module -- 3.2 Improvement of Loss Function -- 3.3 False Detection Handle Based on Category Unification -- 4 Experiment -- 4.1 Test Environment -- 4.2 Evaluation Indicators -- 4.3 Results and Analysis -- 5 Conclusion -- References
A Novel Autoencoder for Task-Driven Object Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Salient Object Segmentation -- 2.2 Attention Mechanism -- 3 Architecture -- 3.1 Network Architecture Overview -- 3.2 Encoder Module -- 3.3 Decoder Module -- 3.4 Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Performance Comparison on Saliency Datasets -- 5 Conclusions and Future Works -- References -- Feedback Attention-Augmented Bilateral Network for Amodal Instance Segmentation -- 1 Introduction -- 2 Related Work
2.1 Amodal 3D Object Detection -- 2.2 Amodal Instance Segmentation -- 2.3 Attention Mechanism -- 2.4 Feature Fusion in Deep Learning -- 3 Proposed Method -- 3.1 Overview -- 3.2 Feedback Attention-Augmented Network -- 3.3 Spatial Detail Preservation Network -- 3.4 Feature Fusion Module -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Datasets and Evaluation Metrics -- 4.3 Comparing with Other Methods on COCOA Dataset -- 4.4 Ablation Study on COCOA Dataset -- 4.5 Visualization Results on COCOA Dataset -- 4.6 Results on D2SA Dataset -- 5 Conclusion -- References
Squeeze-and-Excitation Block Based Mask R-CNN for Object Instance Segmentation -- 1 Introduction -- 2 Related Research -- 3 Proposed Methodology -- 3.1 Outline -- 3.2 Squeeze-and-Excitation Block -- 3.3 Differences from Conventional Methods -- 4 Experiments -- 4.1 Experimental Methods -- 4.2 Datasets -- 4.3 Experimental Environment -- 4.4 Experimental Results -- 4.5 Consideration -- 5 Conclusion -- References -- PointNetX: Part Segmentation Based on PointNet Promotion -- 1 Introduction -- 2 Related Work -- 2.1 Projection-Biassed Approach -- 2.2 Networks that Deal Directly with Point Clouds
Summary
This volume constitutes selected papers presented during the First International Conference on Cognitive Computation and Systems, ICCCS 2022, held in Beijing, China, in October 2022. The 31 papers were thoroughly reviewed and selected from the 75 submissions. The papers are organized in topical sections on computer vision; decision making and cognitive computation; robot and autonomous vehicle
Analysis
Science
Notes
Description based upon print version of record
3 Proposed Method
Includes author index
Online resource; title from PDF title page (SpringerLink, viewed June 5, 2023)
