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Real-Time Object Detection and Classification Using Advanced Sensing Techniques

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 882

Special Issue Editor


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Guest Editor
Department of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: computer vision; machine learning

Special Issue Information

Dear Colleagues, 

Object detection is to locate and identify the category of the target. It is an important basic task in the field of computer vision. Normally, we perform object detection based on RGB sensors. However, for some special scenarios, such as sudden fast-moving objects, objects in harsh environments, tiny objects, small dim objects, etc., traditional RGB-based algorithms may fail to detect these types of objects. Besides, in the actual computer vision systems, such as autonomous driving, we usually need these algorithms to be as efficient as possible to detect objects in real time. The purpose of this special issue is to explore real-time object detection based on advanced sensors.

Potential topics include but are not limited to: 

  • Real-time object detection and tracking systerms;
  • Real-time object detection with event camera/spiking camera;
  • Real-time object detection with LiDAR/Radar;
  • Detection of fast moving objects;
  • Detection of tiny object;
  • Detection of dim small object;
  • Lightweight object detection models;
  • Real-time open-world object detection.

Dr. Chuang Zhu
Guest Editor

Manuscript Submission Information

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Keywords

  • object detection
  • lightweight model
  • real-time algorithm
  • advanced sensors
  • computer vision

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Published Papers (1 paper)

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Research

19 pages, 19052 KiB  
Article
An Image-Free Single-Pixel Detection System for Adaptive Multi-Target Tracking
by Yicheng Peng, Jianing Yang, Yuhao Feng, Shijie Yu, Fei Xing and Ting Sun
Sensors 2025, 25(13), 3879; https://doi.org/10.3390/s25133879 - 21 Jun 2025
Viewed by 574
Abstract
Conventional vision-based sensors face limitations such as low update rates, restricted applicability, and insufficient robustness in dynamic environments with complex object motions. Single-pixel tracking systems offer high efficiency and minimal data redundancy by directly acquiring target positions without full-image reconstruction. This paper proposes [...] Read more.
Conventional vision-based sensors face limitations such as low update rates, restricted applicability, and insufficient robustness in dynamic environments with complex object motions. Single-pixel tracking systems offer high efficiency and minimal data redundancy by directly acquiring target positions without full-image reconstruction. This paper proposes a single-pixel detection system for adaptive multi-target tracking based on the geometric moment and the exponentially weighted moving average (EWMA). The proposed system leverages geometric moments for high-speed target localization, requiring merely 3N measurements to resolve centroids for N targets. Furthermore, the output values of the system are used to continuously update the weight parameters, enabling adaptation to varying motion patterns and ensuring consistent tracking stability. Experimental validation using a digital micromirror device (DMD) operating at 17.857 kHz demonstrates a theoretical tracking update rate of 1984 Hz for three objects. Quantitative evaluations under 1920 × 1080 pixel resolution reveal a normalized root mean square error (NRMSE) of 0.00785, confirming the method’s capability for robust multi-target tracking in practical applications. Full article
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