Electronics

Journal Browser

► Journal Browser

Recent Applications of Object Recognition and Target Detection in Computer Vision

Share This Special Issue

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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.

Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.

Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.

External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.

Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 5199 KB

Open AccessArticle

YOLO-DER: A Dynamic Enhancement Routing Framework for Adverse Weather Vehicle Detection

by Ruilai Gao, Mohd Hasbullah Omar and Massudi Mahmuddin

Electronics 2025, 14(24), 4851; https://doi.org/10.3390/electronics14244851 - 10 Dec 2025

Cited by 1 | Viewed by 1063

Abstract

Deep learning-based vehicle detection methods have achieved impressive performance in favorable conditions. However, their effectiveness declines significantly in adverse weather scenarios, such as fog, rain, and low-illumination environments, due to severe image degradation. Existing approaches often fail to achieve efficient integration between image enhancement and object detection, and typically lack adaptive strategies to cope with diverse degradation patterns. To address these challenges, this paper proposes a novel end-to-end detection framework, You Only Look Once-Dynamic Enhancement Routing (YOLO-DER), which introduces a lightweight Dynamic Enhancement Routing module. This module adaptively selects the optimal enhancement strategy—such as dehazing or brightness correction—based on the degradation characteristics of the input image. It is jointly optimized with the YOLOv12 detector to achieve tight integration of enhancement and detection. Extensive experiments on BDD100K, Foggy Cityscapes, and ExDark demonstrate the superior performance of YOLO-DER, yielding mAP₅₀ scores of 80.8%, 57.9%, and 85.6%, which translate into absolute gains of +3.8%, +2.3%, and +2.9% over YOLOv12 on the respective datasets. The results confirm its robustness and generalization across foggy, rainy, and low-light conditions, providing an efficient and scalable solution for all-weather visual perception in autonomous driving. Full article

(This article belongs to the Special Issue Recent Applications of Object Recognition and Target Detection in Computer Vision)

► Show Figures

Figure 1

34 pages, 2006 KB

Open AccessArticle

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 810

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 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,

{\hat{e}}_{k} (x) = α (x) \cdot e_{k} (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)

► Show Figures

Journal Menu

Journal Browser

Recent Applications of Object Recognition and Target Detection in Computer Vision

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI