Applications of Image Recognition Algorithms

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 1 April 2027 | Viewed by 966

Editors


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1. Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia
2. Faculty of Artificial Intelligence and Cyber Security, Universiti Tehnikal Malaysia Melaka, Hang Tuah Jaya 76100, Melaka, Malaysia
Interests: computer vision; object detection and recognition; explainable artificial intelligent (XAI); machine learning; deep learning; neural network
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Guest Editor
Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
Interests: network science; social network analysis; Internet of Things; logic programming; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image recognition algorithms have become a core enabler of modern intelligent systems, supporting a wide range of real-world applications such as medical screening, industrial inspection, transportation safety, smart cities, environmental monitoring, and remote sensing. At the same time, practical deployment still faces major challenges, such as domain shift across environments and sensors, limited annotations, noisy or low-quality imagery, real-time and edge-computing constraints, and the need for interpretable and trustworthy decisions.

This Special Issue, “Applications of Image Recognition Algorithms”, aims to gather original, high-quality contributions that advance image recognition methods and their practical impact. We welcome manuscripts that propose new recognition algorithms, improve robustness and efficiency, enhance interpretability and reliability, or demonstrate strong application value through comprehensive experiments and reproducible evaluation. The objective of this Special Issue is to bring together leading scientists and practitioners and create an interdisciplinary platform to share computational theories, methodologies, and techniques for image recognition in realistic scenarios.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Image classification (multi-class, multi-label, fine-grained recognition);
  • Object detection, instance segmentation, and scene understanding;
  • Robust recognition under domain shift (weather, illumination, sensor variation);
  • Self-supervised, semi-supervised, and weakly supervised recognition;
  • Knowledge-guided recognition (graphs, ontologies, structured priors);
  • Lightweight recognition for edge devices (pruning, quantization, distillation);
  • Explainable and interpretable recognition (XAI, evidence-based prediction);
  • Uncertainty estimation, calibration, and risk-aware recognition;
  • Adversarial robustness and secure recognition pipelines;
  • Benchmarking, datasets, evaluation protocols, and reproducible pipelines;
  • Medical, industrial, agricultural, transportation, and environmental applications;
  • Remote sensing and satellite image understanding.

We look forward to receiving your contributions.

Dr. Christine Dewi
Dr. Francesco Cauteruccio
Guest Editors

Manuscript Submission Information

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Keywords

  • image recognition
  • computer vision
  • image classification
  • object detection
  • scene understanding
  • edge AI
  • model compression
  • explainable artificial intelligence (XAI)
  • robust learning
  • domain adaptation

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Published Papers (1 paper)

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Research

20 pages, 13777 KB  
Article
MCFusion: A Lightweight RGB-T Pedestrian Detection Method with Progressive Thermal Compensation
by Haokun Li, Haodong Xu and Daheng Chen
Algorithms 2026, 19(6), 468; https://doi.org/10.3390/a19060468 - 8 Jun 2026
Viewed by 507
Abstract
RGB-T pedestrian detection remains challenging under low-light, occluded, crowded, and complex-background conditions. To improve cross-modal feature fusion while maintaining model efficiency, this paper proposes MCFusion, a lightweight RGB-T pedestrian detection method with progressive thermal compensation. MCFusion adopts a dual-branch RGB–thermal feature extraction structure [...] Read more.
RGB-T pedestrian detection remains challenging under low-light, occluded, crowded, and complex-background conditions. To improve cross-modal feature fusion while maintaining model efficiency, this paper proposes MCFusion, a lightweight RGB-T pedestrian detection method with progressive thermal compensation. MCFusion adopts a dual-branch RGB–thermal feature extraction structure and introduces a Modality-Compensated Gated Fusion (MCGF) module at the P4 and P5 semantic stages, which is implemented as a zero-initialized residual compensation mechanism. MCGF uses RGB features as the primary stream and progressively compensates them with thermal auxiliary features through a zero-initialized convolutional gate, reducing the interference caused by direct fusion. In addition, a Lightweight Shared Convolutional Detection Head (LSCD) is adopted to reduce redundant computation in multi-scale prediction. On the LLVIP dataset, MCFusion achieves 95.30% mAP50 and 60.10% mAP50:95 with 5.21 M parameters and 10.50 GFLOPs. Compared with the YOLOv11n RGB baseline, it improves mAP50 and mAP50:95 by 7.50 and 10.70 percentage points, respectively. Experiments on KAIST, ablation studies, and visualization results further demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Applications of Image Recognition Algorithms)
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