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Recent Applications of Object Recognition and Target Detection in Computer...
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Recent Applications of Object Recognition and Target Detection in Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 379

Special Issue Editors

Dr. Yan Gan

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: computer vision; deep learning; artificial intelligence security
Dr. Deqiang Ouyang

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: computer vision; reinforcement learning; embodied intelligence
Dr. Xiangpeng Li

E-Mail Website
Guest Editor
National Elite Institute of Engineering, Chongqing University, Chongqing 400044, China
Interests: computer vision; deepfake detection; embodied intelligence

Special Issue Information

Dear Colleagues,

Object recognition and target detection are fundamental pillars of computer vision, driving transformative advancements across diverse fields such as autonomous systems, intelligent surveillance, medical image analysis, robotics, and augmented reality. Recent breakthroughs in deep learning architectures (e.g., CNNs and transformers), sensor technologies, and computational power have significantly enhanced the accuracy, robustness, and efficiency of these capabilities. This Special Issue aims to showcase cutting-edge research and innovative applications leveraging object recognition and target detection. We particularly seek contributions that demonstrate novel methodologies, address practical challenges in real-world deployment, or explore emerging trends pushing the boundaries of performance. We invite original research highlighting significant recent progress.

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

Advanced deep learning models (CNN, transformer, etc.) for recognition/detection;

Real-time object recognition/target detection in video streams;

Three-dimensional object recognition/target detection and pose estimation;

Small object, occluded object, and adversarial attack detection;

Multi-modal fusion (RGB-D, LiDAR, and thermal) for recognition/detection;

Recognition/detection applications in autonomous driving, robotics, healthcare, security, and industrial automation.

Dr. Yan Gan
Dr. Deqiang Ouyang
Dr. Xiangpeng Li
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 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-blind 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

  • object recognition
  • target detection
  • deep learning

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Published Papers (1 paper)

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Research

34 pages, 2006 KB  
Article
Selective Learnable Discounting in Deep Evidential Semantic Mapping
by Dongfeng Hu, Zhiyuan Li, Junhao Chen and Jian Xu
Electronics 2025, 14(23), 4602; https://doi.org/10.3390/electronics14234602 - 24 Nov 2025
Viewed by 208
Abstract
In autonomous driving and mobile robotics applications, constructing accurate and reliable three-dimensional semantic maps poses significant challenges in resolving conflicts and uncertainties among multi-frame observations in complex environments. Traditional deterministic fusion methods struggle to effectively quantify and process uncertainties in observations, while existing [...] Read more.
In autonomous driving and mobile robotics applications, constructing accurate and reliable three-dimensional semantic maps poses significant challenges in resolving conflicts and uncertainties among multi-frame observations in complex environments. Traditional deterministic fusion methods struggle to effectively quantify and process uncertainties in observations, while existing evidential deep learning approaches, despite providing uncertainty modeling frameworks, still exhibit notable limitations when dealing with spatially varying observation quality. This paper proposes a selective learnable discounting method for deep evidential semantic mapping that introduces a lightweight selective α-Net network based on the EvSemMap framework proposed by Kim and Seo. The network can adaptively detect noisy regions and predict pixel-level discounting coefficients based on input image features. Unlike traditional global discounting strategies, this work employs a theoretically principled scaling discounting formula, e^k(x)=α(x)·ek(x), that conforms to Dempster–Shafer theory, implementing a selective adjustment mechanism that reduces evidence reliability only in noisy regions while preserving original evidence strength in clean regions. Theoretical proofs verify three core properties of the proposed method: evidence discounting under preservation (ensuring no loss of classification accuracy), valid uncertainty redistribution validity (effectively suppressing overconfidence in noisy regions), and optimality of discount coefficients (achieving the matching of the theoretical optimal solution of α*(x)=1−N(X)). Experimental results demonstrate that the method achieves a 43.1% improvement in Expected Calibration Error (ECE) for noisy regions and a 75.4% improvement overall, with α-Net attaining an IoU of 1.0 with noise masks on the constructed synthetic dataset—which includes common real-scenario noise types (e.g., motion blur, abnormal illumination, and sensor noise) and where RGB features correlate with observation quality—thereby fully realizing the selective discounting design objective. Combined with additional optimization via temperature calibration techniques, this method provides an effective uncertainty management solution for deep evidential semantic mapping in complex scenarios. Full article
(This article belongs to the Special Issue Recent Applications of Object Recognition and Target Detection in Computer Vision)
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