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Machine Learning and Deep Learning in Image/Video Processing and Sensing: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1907

Special Issue Editors

Dr. Hongying Liu

E-Mail Website
Guest Editor
Medical College, Tianjin University, Tianjin 300072, China
Interests: machine learning; image processing; SAR
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fanhua Shang

E-Mail Website
Guest Editor
The College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
Interests: machine learning; optimization methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has been a powerful tool for humanity in numerous aspects of daily life. The efforts of scientists have enabled machines and computers to process data and make decisions so that we may live more conveniently. Deep learning is an important subcategory of machine learning. With the advances of the internet and the development of various sensors, the amount of available data is growing rapidly, and the models are becoming increasingly large. These facts have posed significant challenges, prompting questions such as “How can we construct large models? How can we solve large-scale problems with machine learning or deep learning methods? And how can we apply these methods to process various data, e.g., text, images, and videos?”

Following the success of the first edition of our Special Issue on “Machine Learning and Deep Learning in Image/Video Processing and Sensing”, we would like to once again invite our colleagues from across the world to contribute their expertise, insights, and findings in the form of original research articles and reviews for the current Special Issue, entitled “Machine Learning and Deep Learning in Image/Video Processing and Sensing: 2nd Edition”.

This Special Issue will continue focusing on machine learning methods for image/video processing and recognition.

Topics include, but are not limited to, the following:

  • Machine learning methods for image processing;
  • Machine learning methods for image recognition;
  • Machine learning methods for video processing;
  • Deep learning methods for image/video processing;
  • Deep learning methods for image/video recognition;
  • Image analysis and enhancement;
  • Video analysis and enhancement;
  • Data mining methods.

Dr. Hongying Liu
Prof. Dr. Fanhua Shang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image sensing
  • video processing
  • machine learning

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 4416 KB  
Article
Low-Light Monocular Depth Estimation Algorithm Based on Illumination Adaptive Image Enhancement
by Xiaoqian Cao, Yang Wang, Wanyu Li and Weifeng Liu
Sensors 2026, 26(10), 3002; https://doi.org/10.3390/s26103002 - 10 May 2026
Viewed by 556
Abstract
Depth estimation in low-light scenes is an enormous challenge in the field of monocular depth estimation. Although numerous algorithms have attempted to improve their performance in low-light scenarios through a variety of techniques, the inconsistent illumination issue caused by local intense or colored [...] Read more.
Depth estimation in low-light scenes is an enormous challenge in the field of monocular depth estimation. Although numerous algorithms have attempted to improve their performance in low-light scenarios through a variety of techniques, the inconsistent illumination issue caused by local intense or colored light is rarely taken into consideration. To tackle this problem, we proposed an illumination adaptive image enhancement-based low-light depth estimation algorithm (IAIE_LDE) in this paper. Our main contribution is an S-shaped illumination estimation basis illumination adaptive consistent correction model, which is designed to eliminate the edge blurring and depth hole effects in depth maps caused by inconsistent lighting. Meanwhile, a low-light depth estimation architecture consisting of three modules, namely, illumination adaptive correction, low-light image enhancement and depth estimation modules, is constructed and trained. Specifically, the first sub-module is designed to alleviate the illumination inconsistency utilizing the proposed S-shaped illumination adaptive correction model by calculating the corresponding correction coefficients for each pixel according to the estimated illumination; the core module of the classic EnlightGAN algorithm is adopted in the second sub-module to improve the overall brightness of the image and solve the other problems caused by low light; the ZoeDepth model is chosen as our depth estimation sub-module to output a depth map comparable to high-quality illuminated images. Extensive experiments on the widely used Oxford RobotCar and nuScenes datasets indicate superior performance of our method by comparing it with state-of-the-art low-light depth estimation algorithms such as RNW, STEPS, ADDS-DepthNet, and ACDepth, both qualitatively and quantitatively. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image/Video Processing and Sensing: 2nd Edition)
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28 pages, 9019 KB  
Article
SAF-SD: Self-Distillation Object Segmentation Method Based on Sequential Three-Way Mask and Attention Fusion
by Biao Wang, Jun Su, Volodymyr Kochan and Lingyu Yan
Sensors 2026, 26(7), 2170; https://doi.org/10.3390/s26072170 - 31 Mar 2026
Viewed by 339
Abstract
Transformer models have achieved powerful performance in various computer vision tasks. However, their black-box nature severely limits model interpretability and the reliability of real-world applications. Most existing interpretation methods generate explanation maps by perturbing masks from the last layer of the Transformer encoder, [...] Read more.
Transformer models have achieved powerful performance in various computer vision tasks. However, their black-box nature severely limits model interpretability and the reliability of real-world applications. Most existing interpretation methods generate explanation maps by perturbing masks from the last layer of the Transformer encoder, but they often overlook uncertain information in masks and detail loss during upsampling and downsampling, resulting in coarse localization, blurred boundaries, and significant background noise in explanations. To address these issues, this paper proposes a self-distillation object segmentation method based on sequential three-way mask and attention fusion (SAF-SD), targeting salient and camouflaged binary object segmentation tasks (sub-tasks of binary pixel-level segmentation). The method consists of two core modules: the sequential three-way mask (S3WM) module and the attention fusion (AF) module. The S3WM module performs strict threshold filtering on masks generated from the final-layer feature maps of the Transformer, aiming to accurately segment foreground objects from backgrounds via binary pixel-level prediction. The AF module aggregates attention matrices across all Transformer encoder layers to construct a cross-layer relation matrix, capturing global semantic dependencies among image patches (e.g., interactions between foreground, background, and edge regions). It then computes the importance score for each patch, refining details and suppressing noise in the initial explanation results. Extensive experimental results demonstrate that SAF-SD significantly outperforms existing baseline methods across key evaluation metrics. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image/Video Processing and Sensing: 2nd Edition)
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22 pages, 2930 KB  
Article
Developing and Assessing the Performance of a Machine Learning Model for Analyzing Drinking Behaviors in Minipigs for Experimental Research
by Frederik Deutch, Lars Schmidt Hansen, Firas Omar Saleh, Marc Gjern Weiss, Constanca Figueiredo, Cyril Moers, Anna Krarup Keller and Stefan Rahr Wagner
Sensors 2026, 26(2), 402; https://doi.org/10.3390/s26020402 - 8 Jan 2026
Cited by 1 | Viewed by 644
Abstract
Monitoring experimental animals is essential for ethical, scientific, and financial reasons. Conventional observation methods are limited by subjectivity and time constraints. Camera-based monitoring combined with machine learning offers a promising solution for automating the monitoring process. This study aimed to validate and assess [...] Read more.
Monitoring experimental animals is essential for ethical, scientific, and financial reasons. Conventional observation methods are limited by subjectivity and time constraints. Camera-based monitoring combined with machine learning offers a promising solution for automating the monitoring process. This study aimed to validate and assess the performance of a machine learning model for analyzing drinking behavior in minipigs. A novel, vision-based monitoring system was developed and tested to detect drinking behavior in minipigs. The system, based on low-cost Raspberry Pi units, enabled on-site video analysis. A dataset of 5297 images was used to train a YOLOv11n object detection model to identify key features such as pig heads and water faucets. Drinking events were defined by the spatial proximity of these features within video frames. The multi-class object detection model achieved an accuracy of above 97%. Manual validation using human-annotated ground truth on 72 h of video yielded an overall accuracy of 99.7%, with a precision of 99.7%, recall of 99.2%, and F1-score of 99.5%. Drinking patterns for three pigs were analyzed using 216 h of video. The results revealed a bimodal drinking pattern and substantial inter-pig variability. A limitation to the study was chosen methods missing distinguishment between multiple pigs and the absence of quantification of water intake. This study demonstrates the feasibility of a low-cost, computer vision-based system for monitoring drinking behavior in individually housed experimental pigs, supporting earlier detection of illness. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image/Video Processing and Sensing: 2nd Edition)
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