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Eng
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Computer Vision for Autonomous Systems: Perception, Localization, and Mapping
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Computer Vision for Autonomous Systems: Perception, Localization, and Mapping

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 December 2027 | Viewed by 899

Editors

Dr. Kai Zhao

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Shanghai University, Shanghai, China
Interests: computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Changsuk Oh

E-Mail Website
Guest Editor Assistant
Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, USA
Interests: machine learning; medical image analysis

Special Issue Information

Dear Colleagues,

Autonomous systems such as self-driving vehicles, mobile robots, and unmanned aerial platforms rely on computer vision to perceive their surroundings, determine their position, and build representations of the environment for safe navigation. Recent advances in deep learning, sensing technologies, and real-time computing have significantly improved the capabilities of visual perception systems.

This Special Issue will focus on recent advances in computer vision techniques for autonomous systems, with particular emphasis on perception, localization, and mapping. Our goal is to highlight the innovative algorithms, models, and systems that enable machines to interpret visual information and operate autonomously in complex and uncertain environments.

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

  • Visual perception and scene understanding for autonomous systems;
  • Object detection, segmentation, and tracking in dynamic environments;
  • Visual localization and visual–inertial odometry;
  • Simultaneous localization and mapping (SLAM) and visual mapping;
  • Multi-sensor fusion (e.g., camera, LiDAR, radar);
  • Three-dimensional reconstruction and spatial understanding;
  • Deep learning methods for autonomous perception;
  • Robust perception under adverse conditions (e.g., low light, weather, occlusion);
  • Edge computing and real-time vision systems for robotics and autonomous platforms;
  • Benchmark datasets and evaluation methods for autonomous vision systems.

By bringing together contributions from researchers in computer vision, robotics, artificial intelligence, and autonomous systems engineering, this Special Issue aims to advance the development of reliable visual intelligence for next-generation autonomous platforms. We welcome both theoretical advances and practical system implementations that demonstrate improved performance, robustness, and scalability in real-world autonomous applications.

Dr. Kai Zhao
Guest Editor

Dr. Changsuk Oh
Guest Editor Assistant

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. Eng is an international peer-reviewed open access monthly 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 1400 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

  • autonomous systems
  • computer vision
  • visual perception
  • visual SLAM
  • localization and mapping

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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

17 pages, 3198 KB  
Article
YOLOv11-LREP: A Lightweight Detection Method for Water-Surface Floating Objects on Inland Waterways Under Low-Light and Reflection Interference
by Ruicheng Yang, Hailiang Zhao, Yongyi Kong, Yicheng Lai and Jiansen Zhao
Eng 2026, 7(7), 315; https://doi.org/10.3390/eng7070315 - 30 Jun 2026
Viewed by 274
Abstract
Reliable visual detection of small floating objects on the water surface is a prerequisite for environmental monitoring and clean-up tasks performed by unmanned surface vehicles (USVs) on inland waterways. Such scenes are routinely degraded by low illumination at dawn and dusk, strong specular [...] Read more.
Reliable visual detection of small floating objects on the water surface is a prerequisite for environmental monitoring and clean-up tasks performed by unmanned surface vehicles (USVs) on inland waterways. Such scenes are routinely degraded by low illumination at dawn and dusk, strong specular reflections, ripple-induced clutter, and large object-scale variations, which together cause missed detections, false alarms, and unstable localization. Aiming at these practical challenges, this study conducts a scenario-oriented optimization and experimental validation based on the lightweight YOLOv11n detector. We integrate multiple mature attention mechanisms, regression loss functions and data augmentation strategies to develop an improved scheme, YOLOv11-LREP, for floating object detection. The detailed optimizations are as follows: (i) a Coordinate Attention (CoordAtt) module is inserted at the top of the backbone to enhance positional encoding and highlight obstacle-related semantic regions; (ii) three Efficient Channel Attention (ECA) modules are embedded at the multi-scale fusion nodes of the Neck so that reflection- and ripple-induced spurious channel responses can be suppressed at almost no extra cost; (iii) the Powerful-IoU (PIoU) loss replaces the original regression loss to enforce four-side boundary alignment and stabilize convergence on small, blurred-edge targets; and (iv) a joint low-light and reflection augmentation strategy, together with CutMix region-level mixing, broadens the training distribution along the illumination and occlusion axes. Experiments on the public FloW-Img dataset, split into 1200 training and 800 validation images (2024 instances) and run under a fixed random seed (seed = 0, deterministic = true), show that YOLOv11-LREP attains AP50 = 80.1%, AP50:95 = 38.5%, and AP_S = 24.3% with only 2.84 M parameters and 9.3 GFLOPs. On an NVIDIA RTX 4060 Laptop GPU, the model runs at 3.3 ms total per 640 × 640 image (≈303 FPS), satisfying real-time perception requirements while retaining lightweight deployability. The ablation results indicate that different components contribute differently to localization accuracy, small-object sensitivity, and robustness, and that the final configuration provides a balanced trade-off rather than the best value for every individual metric. A systematic threshold sensitivity analysis (F1 fluctuation < 0.2%) demonstrates the stability of the final model. Full article
(This article belongs to the Special Issue Computer Vision for Autonomous Systems: Perception, Localization, and Mapping)
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23 pages, 77558 KB  
Article
FAFMNet: Feature Attention Fusion Multimodal Network of Road Potholes for Mobile Robot
by Jianji Fu, Hongyi Li, Qi Liu, Gaofeng Zheng, Jianhuan Zhang, Jin Jiang and Chentao Zhang
Eng 2026, 7(6), 289; https://doi.org/10.3390/eng7060289 - 11 Jun 2026
Viewed by 301
Abstract
Road potholes pose a considerable threat to mobile robots, which are generally less stable than conventional vehicles and may become trapped or overturned when traversing damaged road surfaces. Accurate semantic segmentation of road potholes is therefore essential for safe and reliable robot navigation. [...] Read more.
Road potholes pose a considerable threat to mobile robots, which are generally less stable than conventional vehicles and may become trapped or overturned when traversing damaged road surfaces. Accurate semantic segmentation of road potholes is therefore essential for safe and reliable robot navigation. To address this requirement, multimodal fusion methods using RGB (Red, Green, Blue) and disparity images have been developed for pothole detection. Nevertheless, these methods still face challenges in detecting small potholes and delineating their boundaries precisely. To overcome these limitations, we propose a novel multimodal fusion network for road-pothole semantic segmentation. Specifically, we design a feature fusion module that integrates global context and local details to fully exploit the complementary information provided by RGB and disparity images. This design improves multimodal feature interaction and enhances boundary segmentation accuracy. Furthermore, we develop three feature attention fusion modules by incorporating multiple complementary attention mechanisms into the fusion module. These modules improve small-pothole detection by focusing on informative features, emphasizing target regions, and reducing information loss. We evaluate the proposed network on a small-pothole subset of Pothole-600 under identical hardware settings and backbone configurations for all experimental models. On the small-pothole subset of Pothole-600, FAFMNet achieves 90.22% mPre, 92.32% mRec, 98.73% mAcc, 91.26% mF1, and 83.93% mIoU, outperforming the state-of-the-art method by 1.87 percentage points in mF1 and 3.12 percentage points in mIoU. A paired statistical test over three independent runs further confirms that the improvement over the baseline is statistically significant (p<0.05). Full article
(This article belongs to the Special Issue Computer Vision for Autonomous Systems: Perception, Localization, and Mapping)
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