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Electronics
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Computer Vision and Machine Learning for Autonomous Intelligent Systems
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Computer Vision and Machine Learning for Autonomous Intelligent Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 326

Editor

Dr. Xiaoyan Li

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: computer vision; embodied AI

Special Issue Information

Dear Colleagues,

The field of autonomous intelligent systems is undergoing a paradigm shift, driven by rapid advancements in computer vision and machine learning. As we move beyond simple automation, the challenge lies in endowing agents—particularly autonomous vehicles, mobile robots, and humanoid platforms—with the ability to perceive, understand, and interact with complex, dynamic 3D environments in real time.

The aim of this Special Issue is to bring together innovative research at the intersection of visual perception and deep learning to enhance the autonomy of intelligent systems. We are particularly interested in methodologies that bridge the gap between raw sensory data and high-level cognitive reasoning, enabling robust navigation and interaction in the real world. Furthermore, we seek contributions focusing on the translation from metric 3D reconstruction to actionable semantic understanding, required for tasks such as vision–language navigation and complex manipulation. This includes the application of large foundation models to robotics (Embodied AI), advancements in 3D scene understanding, and the integration of vision with other modalities for improved decision-making.

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

  • Three-dimensional perception and scene understanding;
  • Vision–language navigation (VLN) and semantic navigation;
  • Manipulation in unstructured environments;
  • Sim-to-real transfer learning for intelligent systems;
  • Deep learning for humanoid robot control and locomotion.

I look forward to receiving your contributions.

Dr. Xiaoyan Li
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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

  • scene understanding
  • autonomous navigation
  • humanoid robotics
  • embodied AI

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Published Papers (1 paper)

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Research

26 pages, 33755 KB  
Article
MFP-YOLOv11: A Multi-Scale Feature Fusion YOLOv11 Variant for Object Detection in Complex Road Scenes
by Junshuai Wang, Mingjing Li, Linlin Liu, Kaijie Li, Zengzhi Zhao and Haijiao Yun
Electronics 2026, 15(14), 2986; https://doi.org/10.3390/electronics15142986 - 8 Jul 2026
Abstract
As autonomous-driving scenarios become increasingly complex, object detection in road environments remains challenging, especially for small-scale, visually ambiguous, and partially occluded targets. These difficulties are closely related to the loss of fine-grained spatial information caused by repeated downsampling and the limited consistency of [...] Read more.
As autonomous-driving scenarios become increasingly complex, object detection in road environments remains challenging, especially for small-scale, visually ambiguous, and partially occluded targets. These difficulties are closely related to the loss of fine-grained spatial information caused by repeated downsampling and the limited consistency of multi-scale feature fusion. To address these issues, this paper proposes MFP-YOLOv11 (Multi-dimensional Focused P2 YOLOv11), a YOLOv11-based detector with enhanced multi-scale feature fusion for complex road-scene object detection. The proposed method improves the YOLOv11 architecture from the perspectives of high-resolution feature preservation, deep contextual representation, and multi-scale feature fusion consistency. Specifically, a Multi-Scale Dynamic Alignment Feature Fusion module (MDAF) is designed as the main fusion component to enhance multi-scale feature representation by modelling channel-, spatial-, and scale-level relationships among features at different resolutions. In addition, C3Ghost is selectively employed in shallow high-resolution stages to partially offset the additional computational cost introduced by the enhanced architecture, AIFI-RepBN is introduced to strengthen deep contextual representation, and Detect-P2 is added to provide high-resolution prediction compensation for small-scale object detection. Experimental results on the SODA10M dataset show that MFP-YOLOv11 achieves an mAP@0.5 of 0.697 and an mAP@0.5:0.95 of 0.483, corresponding to absolute gains of 7.0 and 5.7 percentage points over the YOLOv11 baseline, respectively. Comparative experiments, ablation studies, component-wise analysis, and qualitative visualizations show the contribution of the proposed modifications to detection performance in representative complex road scenes. Cross-dataset testing on the KITTI dataset further evaluates the performance of the proposed method under heterogeneous road-scene distributions. Overall, MFP-YOLOv11 improves Recall and mAP in complex road-scene object detection, while introducing higher computational complexity than the original baseline model. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning for Autonomous Intelligent Systems)
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