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Deep Learning and Digital Image Processing
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Deep Learning and Digital Image Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2847

Special Issue Editor

Dr. Xingjian Gu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Interests: machine learning; remote sensing image processing; video understanding; object tracking

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence, deep learning technology, as an important subset of AI, enables models to autonomously infer results from structured datasets without the need for explicit human intervention. Deep learning has far surpassed traditional techniques and even human capabilities. Deep learning has achieved significant results in various image processing tasks, including image classification, object detection, image segmentation, and image enhancement.

This Special Issue on “Deep Learning and Digital Image Processing” seeks high-quality research focusing on the basic principles, core algorithms, network structure designs, and specific applications in image processing of deep learning. Topics include, but are not limited to, the following:

  1. Deep learning for image super-resolution.
  2. Object detection, tracking, and recognition.
  3. Deep learning for image segmentation.
  4. Neural networks and deep learning.
  5. Low-level visual understanding and image processing.
  6. Feature extraction and feature selection.
  7. Document analysis and recognition.
  8. Activity recognition.
  9. Multimedia analysis and inference.
  10. Remote sensing image interpretation.
  11. Medical image processing and analysis.
  12. Visual issues in multimodal information processing.
  13. Time series analysis.

Dr. Xingjian Gu
Guest Editor

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. Applied Sciences 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 2400 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

  • deep learning
  • image process
  • classification
  • video understand
  • remote sensing
  • medical image
  • multi modal
  • document analysis
  • time series analysis

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Published Papers (4 papers)

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Research

18 pages, 2803 KiB  
Article
Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
by Furong Peng, Kangjiang Hao and Xuan Lu
Appl. Sci. 2025, 15(9), 4769; https://doi.org/10.3390/app15094769 - 25 Apr 2025
Viewed by 70
Abstract
Foreign object detection on coal mine conveyor belts is crucial for ensuring operational safety and efficiency. However, applying deep learning to this task is challenging due to variations in camera perspectives, which alter the appearance of foreign objects and their surrounding environment, thereby [...] Read more.
Foreign object detection on coal mine conveyor belts is crucial for ensuring operational safety and efficiency. However, applying deep learning to this task is challenging due to variations in camera perspectives, which alter the appearance of foreign objects and their surrounding environment, thereby hindering model generalization. Despite these viewpoint changes, certain core characteristics of foreign objects remain consistent. Specifically, (1) foreign objects must be located on the conveyor belt, and (2) their surroundings are predominantly coal, rather than other objects. To leverage these stable features, we propose the Camera-Adaptive Foreign Object Detection (CAFOD) model, designed to improve cross-camera generalization. CAFOD incorporates three main strategies: (1) Multi-View Data Augmentation (MVDA) simulates viewpoint variations during training, enabling the model to learn robust, viewpoint-invariant features; (2) Context Feature Perception (CFP) integrates local coal background information to reduce false detections outside the conveyor belt; and (3) Conveyor Belt Area Loss (CBAL) enforces explicit attention to the conveyor belt region, minimizing background interference. We evaluate CAFOD on a dataset collected from real coal mines using three distinct cameras. Experimental results demonstrate that CAFOD outperforms State-of-the-Art object detection methods, achieving superior accuracy and robustness across varying camera perspectives. Full article
(This article belongs to the Special Issue Deep Learning and Digital Image Processing)
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14 pages, 6013 KiB  
Article
FE-P Net: An Image-Enhanced Parallel Density Estimation Network for Meat Duck Counting
by Huanhuan Qin, Wensheng Teng, Mingzhou Lu, Xinwen Chen, Ye Erlan Xieermaola, Saydigul Samat and Tiantian Wang
Appl. Sci. 2025, 15(7), 3840; https://doi.org/10.3390/app15073840 - 1 Apr 2025
Viewed by 221
Abstract
Traditional object detection methods for meat duck counting suffer from high manual costs, low image quality, and varying object sizes. To address these issues, this paper proposes FE-P Net, an image enhancement-based parallel density estimation network that integrates CNNs with Transformer models. FE-P [...] Read more.
Traditional object detection methods for meat duck counting suffer from high manual costs, low image quality, and varying object sizes. To address these issues, this paper proposes FE-P Net, an image enhancement-based parallel density estimation network that integrates CNNs with Transformer models. FE-P Net employs a Laplacian pyramid to extract multi-scale features, effectively reducing the impact of low-resolution images on detection accuracy. Its parallel architecture combines convolutional operations with attention mechanisms, enabling the model to capture both global semantics and local details, thus enhancing its adaptability across diverse density scenarios. The Reconstructed Convolution Module is a crucial component that helps distinguish targets from backgrounds, significantly improving feature extraction accuracy. Validated on a meat duck counting dataset in breeding environments, FE-P Net achieved 96.46% accuracy in large-scale settings, demonstrating state-of-the-art performance. The model shows robustness across various densities, providing valuable insights for poultry counting methods in agricultural contexts. Full article
(This article belongs to the Special Issue Deep Learning and Digital Image Processing)
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14 pages, 9996 KiB  
Article
Road Extraction from Remote Sensing Images Using a Skip-Connected Parallel CNN-Transformer Encoder-Decoder Model
by Linger Gui, Xingjian Gu, Fen Huang, Shougang Ren, Huanhuan Qin and Chengcheng Fan
Appl. Sci. 2025, 15(3), 1427; https://doi.org/10.3390/app15031427 - 30 Jan 2025
Viewed by 1063
Abstract
Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature learning enabled by inductive biases, deliver impressive results. However, they face [...] Read more.
Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature learning enabled by inductive biases, deliver impressive results. However, they face challenges in capturing global context and accurately extracting the linear features of roads due to their localized receptive fields. To address these shortcomings of traditional methods, this paper proposes a novel parallel encoder architecture that integrates a CNN Encoder Module (CEM) with a Transformer Encoder Module (TEM). The integration combines the CEM’s strength in local feature extraction with the TEM’s ability to incorporate global context, achieving complementary advantages and overcoming limitations of both Transformers and CNNs. Furthermore, the architecture also includes a Linear Convolution Module (LCM), which uses linear convolutions tailored to the shape and distribution of roads. By capturing image features in four specific directions, the LCM significantly improves the model’s ability to detect and represent global and linear road features. Experimental results demonstrate that our proposed method achieves substantial improvements on the German-Street Dataset and the Massachusetts Roads Dataset, increasing the Intersection over Union (IoU) of road class by at least 3% and the overall F1 score by at least 2%. Full article
(This article belongs to the Special Issue Deep Learning and Digital Image Processing)
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16 pages, 3285 KiB  
Article
Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model
by Dongming Li, Zhenkun Zhao, Yingying Yin and Chunxi Zhao
Appl. Sci. 2024, 14(22), 10613; https://doi.org/10.3390/app142210613 - 18 Nov 2024
Cited by 1 | Viewed by 800
Abstract
Ginseng is a common medicinal herb with high value due to its unique medicinal properties. Traditional methods for classifying ginseng rely heavily on manual judgment, which is time-consuming and subjective. In contrast, deep learning methods can objectively learn the features of ginseng, saving [...] Read more.
Ginseng is a common medicinal herb with high value due to its unique medicinal properties. Traditional methods for classifying ginseng rely heavily on manual judgment, which is time-consuming and subjective. In contrast, deep learning methods can objectively learn the features of ginseng, saving both labor and time. This experiment proposes a ginseng-grade classification model based on an improved ResNeXt50 model. First, each convolutional layer in the Bottleneck structure is replaced with the corresponding Ghost module, reducing the model’s computational complexity and parameter count without compromising performance. Second, the SE attention mechanism is added to the model, allowing it to capture feature information more accurately and precisely. Next, the ELU activation function replaces the original ReLU activation function. Then, the dataset is augmented and divided into four categories for model training. A model suitable for ginseng grade classification was obtained through experimentation. Compared with classic convolutional neural network models ResNet50, AlexNet, iResNet, and EfficientNet_v2_s, the accuracy improved by 10.22%, 5.92%, 4.63%, and 3.4%, respectively. The proposed model achieved the best results, with a validation accuracy of up to 93.14% and a loss value as low as 0.105. Experiments have shown that this method is effective in recognition and can be used for ginseng grade classification research. Full article
(This article belongs to the Special Issue Deep Learning and Digital Image Processing)
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