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Real-Time Object Detection and Classification Using Advanced Sensing Techniques
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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: 31 October 2026 | Viewed by 6386

Special Issue Editor

Dr. Chuang Zhu

E-Mail Website
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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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. Sensors 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 2600 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 detection
  • lightweight model
  • real-time algorithm
  • advanced sensors
  • computer vision

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Published Papers (5 papers)

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Research

29 pages, 19163 KB  
Article
Real-Time Small Retail Product Detection in Low-Light Intelligent Cabinets Under Complex Backgrounds
by Moushiqi Yang, Junjie Cai, Yuanyuan Yang, Jian Chen and Kai Xie
Sensors 2026, 26(10), 3264; https://doi.org/10.3390/s26103264 - 21 May 2026
Abstract
Intelligent retail cabinets require accurate and real-time detection of small retail products in complex environments, particularly under low-light conditions and large-scale variations. However, existing object detection methods often suffer from insufficient feature representation and unstable performance in small-object retail commodity recognition tasks under [...] Read more.
Intelligent retail cabinets require accurate and real-time detection of small retail products in complex environments, particularly under low-light conditions and large-scale variations. However, existing object detection methods often suffer from insufficient feature representation and unstable performance in small-object retail commodity recognition tasks under low illumination and complex backgrounds. To address these challenges, this paper proposes a real-time small retail product detection framework based on YOLOv26 for low-light intelligent cabinet environments, aiming to improve detection accuracy, robustness, and deployment efficiency. A C3k2-enhanced multi-scale feature extraction module is introduced to strengthen feature representation for small retail products, while a novel detection head integrates minimum-resolution feature layers and an Efficient Multi-scale Attention (EMA) mechanism to enhance feature fitting ability under low-light conditions. In addition, convolution-based downsampling and Content-Aware ReAssembly of Features module (CARAFE) is adopted to improve feature fusion efficiency and reduce computational overhead. Experimental results on the RPC commodity dataset and the 6th Commodity Recognition Competition dataset demonstrate that the proposed method achieves improved detection performance compared with baseline models, with a 0.9% increase in Recall and a 0.2% improvement in mean Average Precision at IoU threshold 0.50 (mAP@50) while maintaining competitive mean Average Precision averaged over IoU thresholds from 0.50 to 0.95 (mAP@50-95). While the GFLOPS value rose from 5.8 to 6.3, deployment on the Jetson Nano platform achieves 25 FPS, demonstrating real-time detection capability in intelligent retail environments. The proposed framework provides a reliable and deployable solution for small retail product detection in low-light intelligent cabinet systems. Full article
(This article belongs to the Special Issue Real-Time Object Detection and Classification Using Advanced Sensing Techniques)
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36 pages, 4079 KB  
Article
FEGW-YOLO: A Feature-Complexity-Guided Lightweight Framework for Real-Time Multi-Crop Detection with Advanced Sensing Integration on Edge Devices
by Yaojiang Liu, Hongjun Tian, Yijie Yin, Yuhan Zhou, Wei Li, Yang Xiong, Yichen Wang, Zinan Nie, Yang Yang, Dongxiao Xie and Shijie Huang
Sensors 2026, 26(4), 1313; https://doi.org/10.3390/s26041313 - 18 Feb 2026
Cited by 2 | Viewed by 735
Abstract
Real-time object detection on resource-constrained edge devices remains a critical challenge in precision agriculture and autonomous systems, particularly when integrating advanced multi-modal sensors (RGB-D, thermal, hyperspectral). This paper introduces FEGW-YOLO, a lightweight detection framework explicitly designed to bridge the efficiency-accuracy gap for fine-grained [...] Read more.
Real-time object detection on resource-constrained edge devices remains a critical challenge in precision agriculture and autonomous systems, particularly when integrating advanced multi-modal sensors (RGB-D, thermal, hyperspectral). This paper introduces FEGW-YOLO, a lightweight detection framework explicitly designed to bridge the efficiency-accuracy gap for fine-grained visual perception on edge hardware while maintaining compatibility with multiple sensor modalities. The core innovation is a Feature Complexity Descriptor (FCD) metric that enables adaptive, layer-wise compression based on the information-bearing capacity of network features. This compression-guided approach is coupled with (1) Feature Engineering-driven Ghost Convolution (FEG-Conv) for parameter reduction, (2) Efficient Multi-Scale Attention (EMA) for compensating compression-induced information loss, and (3) Wise-IoU loss for improved localization in dense, occluded scenes. The framework follows a principled “Compress, Compensate, and Refine” philosophy that treats compression and compensation as co-designed objectives rather than isolated knobs. Extensive experiments on a custom strawberry dataset (11,752 annotated instances) and cross-crop validation on apples, tomatoes, and grapes demonstrate that FEGW-YOLO achieves 95.1% mAP@0.5 while reducing model parameters by 54.7% and computational cost (GFLOPs) by 53.5% compared to a strong YOLO-Agri baseline. Real-time inference on NVIDIA Jetson Xavier achieves 38 FPS at 12.3 W, enabling 40+ hours of continuous operation on typical agricultural robotic platforms. Multi-modal fusion experiments with RGB-D sensors demonstrate that the lightweight architecture leaves sufficient computational headroom for parallel processing of depth and visual data, a capability essential for practical advanced sensing systems. Field deployment in commercial strawberry greenhouses validates an 87.3% harvesting success rate with a 2.1% fruit damage rate, demonstrating feasibility for autonomous systems. The proposed framework advances the state-of-the-art in efficient agricultural sensing by introducing a principled metric-guided compression strategy, comprehensive multi-modal sensor integration, and empirical validation across diverse crop types and real-world deployment scenarios. This work bridges the gap between laboratory research and practical edge deployment of advanced sensing systems, with direct relevance to autonomous harvesting, precision monitoring, and other resource-constrained agricultural applications. Full article
(This article belongs to the Special Issue Real-Time Object Detection and Classification Using Advanced Sensing Techniques)
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23 pages, 4804 KB  
Article
Particle Image Velocimetry Algorithm Based on Spike Camera Adaptive Integration
by Xiaoqiang Li, Changxu Wu, Yichao Wang, Hongyuan Li, Yuan Li, Tiejun Huang, Yuhao Huang and Pengyu Lv
Sensors 2025, 25(20), 6468; https://doi.org/10.3390/s25206468 - 19 Oct 2025
Viewed by 1666
Abstract
In particle image velocimetry (PIV), overexposure is particularly common in regions with high illumination. In particular, strong scattering or background reflection at the liquid–gas interface will make the overexposure phenomenon more obvious, resulting in local pixel saturation, which will significantly reduce the particle [...] Read more.
In particle image velocimetry (PIV), overexposure is particularly common in regions with high illumination. In particular, strong scattering or background reflection at the liquid–gas interface will make the overexposure phenomenon more obvious, resulting in local pixel saturation, which will significantly reduce the particle image quality, and thus reduce the particle recognition rate and the accuracy of velocity field estimation. This study addresses the overexposure challenges in particle image velocimetry applications, mainly to address the challenge that the velocity field cannot be measured due to the difficulty in effectively detecting particles in the exposed area. In order to address the challenge of overexposure, this paper does not use traditional frame-based high-speed cameras, but instead proposes a particle image velocimetry algorithm based on adaptive integral spike camera data using a neuromorphic vision sensor (NVS). Specifically, by performing target-background segmentation on high-frequency digital spike signals, the method suppresses high illumination background regions and thus effectively mitigates overexposure. Then the spike data are further adaptively integrated based on both regional background illumination characteristics and the spike frequency features of particles with varying velocities, resulting in high signal-to-noise ratio (SNR) reconstructed particle images. Flow field computation is subsequently conducted using the reconstructed particle images, with validation through both simulation and experiment. In simulation, in the overexposed area, the average flow velocity estimation error of frame-based cameras is 8.594 times that of spike-based cameras. In the experiments, the spike camera successfully captured continuous high-density particle trajectories, yielding measurable and continuous velocity fields. Experimental results demonstrate that the proposed particle image velocimetry algorithm based on the adaptive integration of the spike camera effectively addresses overexposure challenges caused by high illumination of the liquid–gas interface in flow field measurements. Full article
(This article belongs to the Special Issue Real-Time Object Detection and Classification Using Advanced Sensing Techniques)
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11 pages, 4334 KB  
Communication
Real-Time Object Classification via Dual-Pixel Measurement
by Jianing Yang, Ran Chen, Yicheng Peng, Lingyun Zhang, Ting Sun and Fei Xing
Sensors 2025, 25(18), 5886; https://doi.org/10.3390/s25185886 - 20 Sep 2025
Cited by 1 | Viewed by 874
Abstract
Achieving rapid and accurate object classification holds significant importance in various domains. However, conventional vision-based techniques suffer from several limitations, including high data redundancy and strong dependence on image quality. In this work, we present a high-speed, image-free object classification method based on [...] Read more.
Achieving rapid and accurate object classification holds significant importance in various domains. However, conventional vision-based techniques suffer from several limitations, including high data redundancy and strong dependence on image quality. In this work, we present a high-speed, image-free object classification method based on dual-pixel measurement and normalized central moment invariants. Leveraging the complementary modulation capability of a digital micromirror device (DMD), the proposed system requires only five tailored binary illumination patterns to simultaneously extract geometric features and perform classification. The system can achieve a classification update rate of up to 4.44 kHz, offering significant improvements in both efficiency and accuracy compared to traditional image-based approaches. Numerical simulations verify the robustness of the method under similarity transformations—including translation, scaling, and rotation—while experimental validations further demonstrate reliable performance across diverse object types. This approach enables real-time, low-data throughput, and reconstruction-free classification, offering new potential for optical computing and edge intelligence applications. Full article
(This article belongs to the Special Issue Real-Time Object Detection and Classification Using Advanced Sensing Techniques)
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19 pages, 19052 KB  
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
Cited by 6 | Viewed by 2101
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
(This article belongs to the Special Issue Real-Time Object Detection and Classification Using Advanced Sensing Techniques)
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