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Recent Advances in Imaging Sensors: Integration with Machine Learning and Artificial Intelligence

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1428

Special Issue Editors

Dr. Ning Chen

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: mechanical system dynamics modeling; simulation and optimization design; machine learning methods and their applications
Dr. Hang Zhang

E-Mail Website
Guest Editor
Changsha University of science and technology, Changsha 410114, China
Interests: machine vision; anomaly detection
Prof. Dr. Jian Liu

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: intelligent manufacturing; machine vision

Special Issue Information

Dear Colleagues,

Recently, there has been growing interest in the field of imaging sensors, particularly regarding its integration of machine learning and artificial intelligence. An image sensor is a device that can convert optical images into electronic signals, possessing the advantages of high resolution, low noise, a large dynamic range and an extensive potential for integration. Recent advances in this field have made various applications available, including effective intelligent sensors for industrial machine vision, medical imaging, intelligent transportation and so on.

This Special Issue therefore aims to garner original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of image sensors.

Potential topics include, but are not limited to, the following:

  • Intelligent sensors;
  • Industrial machine vision;
  • Anomaly detection;
  • Medical imaging;
  • Object detection for intelligent transportation;
  • Deep learning for image processing;
  • Visual large models.

Dr. Ning Chen
Dr. Hang Zhang
Prof. Dr. Jian Liu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • intelligent sensors
  • industrial machine vision
  • anomaly detection
  • medical imaging
  • object detection for intelligent transportation
  • deep learning for image processing
  • visual large models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

27 pages, 10153 KiB  
Article
PSMDet: Enhancing Detection Accuracy in Remote Sensing Images Through Self-Modulation and Gaussian-Based Regression
by Jiangang Zhu, Yang Ruan, Donglin Jing, Qiang Fu and Ting Ma
Sensors 2025, 25(5), 1285; https://doi.org/10.3390/s25051285 - 20 Feb 2025
Cited by 1 | Viewed by 443
Abstract
Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms [...] Read more.
Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms at the backbone, feature pyramid network (FPN), and detection head stages to address these issues. The backbone network utilizes a reparameterized large kernel network (RLK-Net) to enhance multi-scale feature extraction. At the same time, the adaptive perception network (APN) achieves accurate feature alignment through a self-attention mechanism. Additionally, a Gaussian-based bounding box representation and smooth relative entropy (smoothRE) regression loss are introduced to address traditional bounding box regression challenges, such as discontinuities and inconsistencies. Experimental validation on the HRSC2016 and UCAS-AOD datasets demonstrates the framework’s robust performance, achieving the mean Average Precision (mAP) scores of 90.69% and 89.86%, respectively. Although validated on ORSIs, the proposed framework is adaptable for broader applications, such as autonomous driving in intelligent transportation systems and defect detection in industrial vision, where high-precision object detection is essential. These contributions provide theoretical and technical support for advancing intelligent image sensor-based applications across multiple domains. Full article
(This article belongs to the Special Issue Recent Advances in Imaging Sensors: Integration with Machine Learning and Artificial Intelligence)
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15 pages, 3018 KiB  
Article
LDDP-Net: A Lightweight Neural Network with Dual Decoding Paths for Defect Segmentation of LED Chips
by Jie Zhang, Ning Chen, Mengyuan Li, Yifan Zhang, Xinyu Suo, Rong Li and Jian Liu
Sensors 2025, 25(2), 425; https://doi.org/10.3390/s25020425 - 13 Jan 2025
Viewed by 693
Abstract
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field [...] Read more.
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss. In addition, dual decoding paths consisting of a coarse decoding path and a fine-grained decoding path in parallel are developed. Specifically, the former employs a straightforward upsampling approach, emphasizing macro information. The latter is more detail-oriented, using multiple pooling and convolution techniques to focus on fine-grained information after deconvolution. Moreover, the integration of intermediate-layer features into the upsampling operation enhances boundary segmentation. Experimental results demonstrate that LDDP-Net achieves an mIoU (mean Intersection over Union) of 90.29% on the chip dataset, with parameter numbers and FLOPs (Floating Point Operations) of 2.98 M and 2.24 G, respectively. Comparative analyses with advanced methods reveal varying degrees of improvement, affirming the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Recent Advances in Imaging Sensors: Integration with Machine Learning and Artificial Intelligence)
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