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Image Processing and Analysis for Object Detection: 3rd Edition

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Special Issue Editor

Special Issue Information

Dear Colleagues,

In recent years, there has been a huge rise in interest in the development of deep learning techniques for computer vision. As deep learning comes to encompass almost all fields of science and engineering, computer vision remains one of its primary application areas. Specifically, the use of deep learning to handle computer vision tasks has led to numerous unprecedented applications, such as high-accuracy object detection, visual tracking, image segmentation, image/video super-resolution, satellite image processing, and saliency object detection, which cannot achieve promising performance through the use of conventional methods.

This Special Issue aims to cover the latest advances in the field of computer vision, involving the use of sensors (such as cameras, video cameras, drones, etc.) for image acquisition, the use of deep learning methods, and a special focus on low-level and high-level computer vision tasks. Original research and review articles are welcome to be submitted. Potential topics may include, but are not limited to, the following:

Image/video super-resolution with deep learning approaches;
Object detection, visual tracking, and image/video segmentation with
deep learning approaches;
Supervised and unsupervised learning for image/video processing;
Satellite image processing with deep learning techniques;
Low-light image enhancement using deep learning approaches.

Prof. Dr. Kaihua Zhang
Guest Editor

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Keywords

augmented reality
artificial intelligence
computer vision
classification algorithms
defect detection
deep learning
feature extraction
image processing
image classification
image super-resolution
machine vision
object detection, tracking, and recognition techniques
semantic segmentation
sensing technologies
sensor fusion and technologies
visual tracking
vision sensors
video classification

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21 pages, 3489 KB

Open AccessArticle

GA-YOLOv11: A Lightweight Subway Foreign Object Detection Model Based on Improved YOLOv11

by Ning Guo, Min Huang and Wensheng Wang

Sensors 2025, 25(19), 6137; https://doi.org/10.3390/s25196137 - 4 Oct 2025

Viewed by 603

Abstract

Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 3rd Edition)

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