Deep Learning for Computer Vision Application

1. Introduction

Artificial intelligence (AI) methodologies, particularly deep neural networks—often referred to as deep learning models—have emerged as the foundational techniques for addressing computer vision tasks across a broad spectrum of applications. Deep learning [1], a subset of AI, leverages large datasets and complex algorithms to train models capable of recognizing patterns and making decisions with minimal human intervention. This technology is characterized by its use of multiple layers within neural networks, which enables the processing and interpretation of vast amounts of data with high accuracy and efficiency. The introduction and evolution of these sophisticated deep learning frameworks have facilitated unprecedented levels of automation in the autonomous pattern recognition of image data. These advancements have profoundly impacted everyday life, evident in the seamless functionality of systems such as Google Photos, which automatically sorts and retrieves images, as well as the sophisticated navigation capabilities of autonomous vehicles. Deep learning models have demonstrated exceptional performance in tasks such as image classification, object detection, and facial recognition, transforming the landscape of computer vision—a field dedicated to enabling machines to interpret and make sense of visual information from the world.

Despite these significant strides, the full potential of these powerful techniques remains untapped across all computer vision tasks. It is imperative that future research endeavors aim to expand the applicability of deep learning in various aspects of life. This can be achieved by focusing on enhanced data acquisition and cleaning processes, which are crucial for ensuring the quality and reliability of inputs fed into deep learning models. Furthermore, pursuing further model optimization, innovative approaches, and in-depth research will enhance the effectiveness and efficiency of AI systems, enabling them to tackle more complex and diverse challenges. In line with these objectives, this Special Issue is particularly interested in exploring novel applications of deep learning within the realm of computer vision. By encouraging the development and integration of cutting-edge deep learning solutions into more facets of our technological landscape, we can unlock new possibilities in areas such as digitalizing construction and industry, healthcare diagnostics, industrial automation, and environmental monitoring. As deep learning continues to evolve, its transformative impact on society and industry will likely expand, driving forward advancements that were once considered beyond reach. Participants were invited to write about one of, but not limited to, the following subjects:

• Image classification using deep learning;
• Object detection using deep learning;
• Semantic and instant segmentation using deep learning;
• Deep learning techniques for generating new images (generative adversarial networks);
• Employing reinforcement learning for computer vision tasks;
• Application of deep learning in the Internet of Things (IoT);
• Application of deep learning in embedded systems, sensor development, and electronics;
• Computer vision tasks using deep learning (medical image processing, remote sensing, hyperspectral imaging, thermal imaging, space and extra-terrestrial observations);
• Image sequence analysis using deep learning;
• Deep learning and computer vision for smart and green buildings, smart industry, and smart devices.

2. An Overview of Articles Published in This Special Issue

Several novel deep learning models for computer vision are proposed in this Special Issue for various computer vision-based engineering problems. A convolutional neural network (CNN)-based approach for fluid motion estimation by [2] leverages correlation coefficients and a multiscale cost volume to address challenges such as shape changes and illumination variations, outperforming existing methods in capturing small-scale motions such as vortices on datasets such as direct numerical simulation (DNS)-turbulent flow, surface quasi-geostrophic (SQG) flow, and Jupiter’s atmosphere. Future improvements might incorporate fluid dynamics into CNN-based estimators. Authors of [3] proposed a system to recognize handwritten Nüshu characters, using the HWNS2023 dataset and a two-stage scheme combining GoogLeNet-tiny and YOLOv5-CBAM models, achieving 99.9% overall accuracy and aiding in preserving Nüshu culture. A bird pose estimation method by [4] combining Vision Transformer (ViT) and HRNet with attention mechanisms, improving keypoint accuracy and excelling in bird pose estimation, contributing to ecological understanding and conservation efforts. In [5], a methodology for multiple object tracking uses Graph Attention Networks (GATs) and proposed track management strategies, improving node feature discriminability and handling occlusions. It demonstrates competitive performance on multiple object tracking (MOT) datasets and indicates potential applications in autonomous driving.

Few articles focused on solving pure daily engineering problems in our lives, using advanced deep learning methods for computer vision applications. Authors of [6] discuss the multi-frequency aggregate diffusion real-time detection, embedding, and tracking (MFAD-RTDETR) model for printed circuit board (PCB) defect detection, achieving high mean average precision (mAP) while reducing model parameters, integrating advanced techniques for detecting small defects, with future work focusing on enhancing real-time detection capabilities. A long short-term memory (LSTM)-based system presented in [7] automates annotation of American football footage, achieving a high Levenshtein similarity index (LSI) of 0.9445, reducing manual analysis effort, and suggesting broader applications in sports analytics. Research in [8] explores SwinLSTM and ConvLSTM models for predicting room fire growth, utilizing vision-based data to overcome sensor limitations, and providing detailed predictions that enhance fire safety measures. Authors of [9] address occlusion challenges in 3D hand–object pose estimation with a framework using hierarchical feature decoupling and the HOCAT module, improving hand pose estimation but noting limitations in object pose estimation, suggesting further research.

This Special Issue also covers the new developments in the field of deep learning for computer vision through two comprehensive literature reviews. Article [10] surveys advancements in video classification driven by deep learning, reviewing network architectures and data augmentation methods, highlighting challenges and progress in handling large-scale datasets and improving model generalization. Authors of [11] present a systematic review on AI and deep learning models for interpreting chest radiographs, discussing their accuracy in diagnosing lung diseases and the need for validation in clinical settings, and suggesting hybrid diagnostic systems for future development.

This Special Issue presents key advancements in deep learning models, significantly enhancing computer vision and engineering applications. Innovations in fluid motion estimation, character recognition, pose estimation, and object tracking demonstrate marked improvements in accuracy and efficiency. Deep learning’s transformative potential is evident in its applications to automating sports analytics, predicting fire growth, and refining medical imaging techniques. Future efforts should aim to integrate physical laws into models, enhance real-time capabilities, and improve the accuracy of object pose estimation. Additionally, there is a need to develop hybrid diagnostic systems and effectively integrate AI models into practical workflows to maximize their impact across diverse fields.