Cover Image

International Conference on Neural Computing for Advanced Applications : 4th International Conference, NCAA 2023, Hefei, China, July 7-9, 2023, Proceedings. Part II / Haijun Zhang, Yinggen Ke, Zhou Wu, Tianyong Hao, Zhao Zhang, Weizhi Meng, Yuanyuan Mu, editors.

The two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023. The 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and...

Full description

Saved in:
Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: NCAA (Conference) Hefei Shi, China ; Online)
Other Authors: Zhang, Haijun (Professor of computer science), Ke, Yinggen, Wu, Zhou (Researcher on optimization and artificial intelligence), Hao, Tianyong, Zhang, Zhao (Computer scientist), Meng, Weizhi, Mu, Yuanyuan
Other title:NCAA 2023
Format: Electronic Conference Proceeding eBook
Language:English
Published: Singapore : Springer, [2023]
Series:Communications in computer and information science ; 1870.
Subjects:
Neural computers > Congresses.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents - Part II
  • Contents - Part I
  • Deep Learning-Driven Pattern Recognition, Computer Vision and Its Industrial Applications
  • Improved YOLOv5s Based Steel Leaf Spring Identification
  • 1 Introduction
  • 2 YOLOv5 Structure and Method Flow
  • 2.1 Steel Leaf Spring Visual Identification Process
  • 2.2 YOLOv5s Network Structure
  • 3 YOLOv5 Recognition Algorithm Improvement
  • 3.1 YOLOv5 Steel Leaf Spring Recognition Based On Migration Learning
  • 3.2 CBAM Convolutional Attention Mechanism
  • 3.3 Network Model Lightweighting
  • 4 Experimental Results and Analysis.
  • 4.1 Ablation Experiments
  • 4.2 Comprehensive Comparison Experiments of Different Target Detection Models
  • 5 Summary
  • References
  • A Bughole Detection Approach for Fair-Faced Concrete Based on Improved YOLOv5
  • 1 Introduction
  • 2 Model Design
  • 2.1 The Network Structure of YOLOv5
  • 2.2 Network Structure Improvement
  • 3 Experimental Settings and Results
  • 3.1 The Experiment Platform
  • 3.2 Data Acquisition and Dataset
  • 3.3 Evaluation Metrics
  • 3.4 Experimental Results and Analysis
  • 4 Conclusion
  • References
  • UWYOLOX: An Underwater Object Detection Framework Based on Image Enhancement and Semi-supervised Learning
  • 1 Introduction
  • 2 UWYOLOX
  • 2.1 Joint Learning-Based Image Enhancement Module (JLUIE)
  • 2.2 Improved Semi-supervised Learning Method for Underwater Object Detection (USTAC)
  • 3 Experiments
  • 3.1 Implementation Details
  • 3.2 Experiment Results
  • 4 Discussion and Conclusion
  • References
  • A Lightweight Sensor Fusion for Neural Visual Inertial Odometry
  • 1 Introduction
  • 2 Relate Work
  • 2.1 VO
  • 2.2 Traditional VIO Methods
  • 2.3 Deep Learning-Based VIO
  • 3 Method
  • 3.1 Attention Mechanism for the Visual Branch
  • 3.2 Lightweight Pose Estimation Module
  • 3.3 Loss Function
  • 4 Experiment
  • 4.1 Dataset
  • 4.2 Experimental Setup and Details
  • 4.3 Main Result
  • 5 Conclusion
  • References
  • A Two-Stage Framework for Kidney Segmentation in Ultrasound Images
  • 1 Introdution
  • 2 Relate Works
  • 2.1 Automated Kidney Ultrasound Segmentation
  • 2.2 Level-Set Function
  • 2.3 Self-correction
  • 3 Method
  • 3.1 Overview
  • 3.2 Shape Aware Dual-Task Multi-scale Fusion Network
  • 3.3 Self-correction Part
  • 4 Experiments
  • 4.1 Dataset and Implementation Details
  • 4.2 Experiment Results
  • 4.3 Ablation Studies
  • 5 Conclusion
  • References
  • Applicability Method for Identification of Power Inspection Evidence in Multiple Business Scenarios
  • 1 Introduction
  • 2 Constructing a Sample Library for Identifying Power Inspection Supporting Materials
  • 3 Text Recognition Based on YOLOv3 Network
  • 4 Network Compression with Structure Design and Knowledge Distillation
  • 5 Experiment and Analysis
  • 5.1 Training Sample Augmentation Quality Assessment
  • 5.2 Model Recognition Results and Analysis

Similar Items