Editorial: Advances in computer vision: from deep learning models to practical applications

摘要

Computer vision has emerged as one of the most transformative fields in artificial intelligence, with deep learning models driving unprecedented advancements in both theoretical understanding and practical applications. Over the past decade, the rapid development of deep learning techniques has enabled machines to perform tasks such as image recognition, object detection, and video analysis with remarkable accuracy and efficiency. However, as the field continues to evolve, there is a growing need to bridge the gap between theoretical models and real-world applications, ensuring that these technologies are powerful but also practical, efficient, and scalable. This Research Topic, “Advances in Computer Vision: From Deep Learning Models to Practical Applications,” is dedicated to exploring the latest innovations in computer vision that address these challenges and push the boundaries of what is achievable.The articles in this collection represent a diverse range of research directions and applications, reflecting the interdisciplinary nature of computer vision. From efficient single-image super-resolution techniques to lightweight network architectures for traffic sign recognition, and from medical image processing to action recognition in autonomous systems, the contributions highlight the versatility and potential of computer vision technologies. Below, we provide a brief overview of the accepted articles, emphasizing their key contributions and practical implications.Efficient and Lightweight Deep Learning Models for Real-World ApplicationsOne of the central themes of this Research Topic is the development of efficient and lightweight deep learning models that can operate effectively in resource-constrained environments. An et al. [1] introduced a lightweight network architecture based on an enhanced LeNet-5 model for traffic sign recognition. By optimizing the network structure and reducing the number of parameters, they achieved state-of-the-art performance on standard benchmarks, making their solution suitable for deployment in real-world autonomous driving systems.Qu et al. [2] proposed an asymmetric large kernel distillation network for single image super-resolution, which leveraged asymmetric kernels to achieve high computational efficiency while maintaining superior performance in image restoration. Their approach demonstrated the importance of balancing model complexity and practical applicability, particularly in scenarios where computational resources are limited.Xie et al. [3] proposed an extremely lightweight pathological myopia instance segmentation method (SMLS-YOLO), which combined attention mechanisms with efficient network design to achieve real-time performance. Their approach was particularly valuable for applications in ophthalmology, where rapid and accurate segmentation was critical for diagnosing and monitoring conditions such as pathological myopia. The integration of attention mechanisms in lightweight models highlighted the importance of optimizing both computational efficiency and accuracy, ensuring that these technologies can be deployed in real-world settings.Deep Learning in Medical Image ProcessingMedical image processing is another area where deep learning has shown tremendous potential. Li et al. [4] explored the use of Swin Transformer-based automatic delineation of the hippocampus in MRI scans for hippocampus-sparing whole-brain radiotherapy. Their work showcased the effectiveness of transformer-based architectures in medical image segmentation, providing a more accurate and automated approach to treatment planning.Tian et al. [5] presented a GAN-guided nuance perceptual attention network (G2NPAN) for multimodal medical fusion image quality assessment. Their work combined generative adversarial networks (GANs) with attention mechanisms to evaluate the quality of fused medical images, ensuring that the outputs were both visually appealing and diagnostically useful. This approach highlighted th