State-of-the-Art Object Detection, Tracking, and Recognition Techniques

Topic Information

Dear Colleagues,

In the rapidly evolving domain of artificial intelligence (AI) and machine learning (ML), object detection, tracking, and recognition techniques have witnessed remarkable advancements, driving breakthroughs across diverse fields such as computer vision, autonomous driving, and security surveillance. Despite these significant strides, the field continues to grapple with several challenges, including ethical concerns over data privacy, the persistent difficulty of cross-domain object recognition, and the hurdles in achieving real-time performance in complex environments marked by heavy occlusion and rapid motion. This Topic, titled "State-of-the-Art Object Detection, Tracking, and Recognition Techniques", delves into these challenges and more, offering a comprehensive overview of the latest developments in the field. It explores topics such as multi-modal data generation, data privacy, federated learning, cross-domain learning, and large models in object detection, tracking, and recognition. By bringing together researchers from multimedia, computer vision, and machine learning fields of study, this Topic aims to foster collaboration, and share insights, methodologies, and applications related to object detection, tracking, and recognition, ultimately pushing the boundaries of this exciting and dynamic field.

Prof. Dr. Mang Ye
Dr. Jingwen Ye
Dr. Cuiqun Chen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Journal of Imaging jimaging	2.7	5.9	2015	18.3 Days	CHF 1800	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	35.8 Days	CHF 1900	Submit

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Published Papers (2 papers)

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All AI Applied Sciences Electronics J. Imaging Sensors IJGI

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20 pages, 3343 KiB  

Open AccessArticle

YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11

by Jie He, Yi Ren, Weibin Li and Wenlin Fu

Appl. Sci. 2025, 15(8), 4535; https://doi.org/10.3390/app15084535 - 20 Apr 2025

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Abstract

Detecting pests and diseases on maize leaves is challenging. This is especially true under complex conditions, such as variable lighting and occlusion. Current methods suffer from low detection accuracy. They also lack sufficient real-time performance. Hence, this study introduces the lightweight detection method [...] Read more.

Detecting pests and diseases on maize leaves is challenging. This is especially true under complex conditions, such as variable lighting and occlusion. Current methods suffer from low detection accuracy. They also lack sufficient real-time performance. Hence, this study introduces the lightweight detection method YOLOv11-RCDWD based on an improved YOLOv11 model. The proposed approach enhances the YOLOv11 model by incorporating the RepLKNet module as the backbone, which significantly enhances the model’s capacity to capture characteristics of maize leaf pests and diseases. Additionally, the CBAM is embedded within the neck feature extraction network to further refine the feature representation to augment the model’s capability to identify and select essential features by introducing attention mechanisms in both the channel and spatial dimensions, thereby improving the accuracy of feature expression. We have also improved the model by incorporating the DynamicHead module, WIoU loss function, and DynamicATSS label assignment strategy, which collectively enhance detection accuracy, efficiency, and robustness through optimized attention mechanisms, better handling of low-quality samples, and dynamic sample selection during training. The experimental findings indicate that the improved YOLOv11-RCDWD model effectively detected pests and diseases on maize leaves. The precision reached 92.6%, while the recall was 85.4%. The F1 score was 88.9%, and the mAP@0.5 and mAP@0.5~0.95 demonstrated an improvement of 4.9% and 9.0% over the baseline YOLOv11s. Notably, the YOLOv11-RCDWD model significantly outperformed other architectures such as Faster R-CNN, SSD, and various models within the YOLO series, demonstrating superior capabilities in terms of detection speed, parameter count, computational efficiency, and memory utilization. This model achieves an optimal balance between detection performance and resource efficiency. Overall, the improved YOLOv11-RCDWD model significantly reduces detection time and memory usage while maintaining high detection accuracy, supporting the automated detection of maize pests and diseases, and offering a robust solution for intelligent monitoring of agricultural pests. Full article

(This article belongs to the Topic State-of-the-Art Object Detection, Tracking, and Recognition Techniques)

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19 pages, 3089 KiB  

Open AccessArticle

Efficient Spiking Neural Network for RGB–Event Fusion-Based Object Detection

by Liangwei Fan, Jingjun Yang, Lei Wang, Jinpu Zhang, Xiangkai Lian and Hui Shen

Electronics 2025, 14(6), 1105; https://doi.org/10.3390/electronics14061105 - 11 Mar 2025

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Abstract

Robust object detection in challenging scenarios remains a critical challenge for autonomous driving systems. Inspired by human visual perception, integrating the complementary modalities of RGB frames and event streams presents a promising approach to achieving robust object detection. However, existing multimodal object detectors [...] Read more.

Robust object detection in challenging scenarios remains a critical challenge for autonomous driving systems. Inspired by human visual perception, integrating the complementary modalities of RGB frames and event streams presents a promising approach to achieving robust object detection. However, existing multimodal object detectors achieve superior performance at the cost of significant computational power consumption. To address this challenge, we propose a novel spiking RGB–event fusion-based detection network (SFDNet), a fully spiking object detector capable of achieving both low-power and high-performance object detection. Specifically, we first introduce the Leaky Integrate-and-Multi-Fire (LIMF) neuron model, which combines soft and hard reset mechanisms to enhance feature representation in SNNs. We then develop a multi-scale hierarchical spiking residual attention network and a lightweight spiking aggregation module for efficient dual-modality feature extraction and fusion. Experimental results on two public multimodal object detection datasets demonstrate that our SFDNet achieves state-of-the-art performance with remarkably low power consumption. The superior performance in challenging scenarios, such as motion blur and low-light conditions, highlights the robustness and effectiveness of SFDNet, significantly advancing the applicability of SNNs for real-world object detection tasks. Full article

(This article belongs to the Topic State-of-the-Art Object Detection, Tracking, and Recognition Techniques)

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