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