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AI in Object Detection—2nd Edition

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 December 2026 | Viewed by 603

Editors


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Guest Editor
School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
Interests: image processing; artificial intelligence; deep learning; target detection; pattern recognition; target recognition
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100811, China
Interests: medical image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Small object detection and tracking are currently among the most challenging and fundamental topics in computer vision. ‘Small objects’ are typically characterized by small size, low contrast, and blurred edges, which collectively complicate the tasks of detection and tracking. In recent years, advancements in this field have facilitated the application of small object detection and tracking in remote sensing imagery across diverse domains, including mineral exploration, precision agriculture, urban planning, forestry management, and disaster assessment. Despite these advancements, several critical challenges persist in real-world applications, particularly in the context of high-resolution remote sensing imagery. Key issues include the difficulty of extracting detailed information from small objects, the trade-off between detection accuracy and computational efficiency, the ability to identify unknown or untrained categories within remote sensing data, and the effective tracking of small objects over time. Addressing these challenges remains essential for advancing the practical utility of small object detection and tracking systems. Therefore, we invite submissions of papers including theoretical research and those on practical applications related to transformer models and deep learning architecture for small object detection related to remote sensing images.

Prof. Dr. Fengping An
Dr. Chuyang Ye
Guest Editors

Manuscript Submission Information

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Keywords

  • object detection
  • object tracking
  • target identification
  • remote sensing
  • deep learning

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Published Papers (1 paper)

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Research

28 pages, 4507 KB  
Article
MS-YOLO: A Satellite Remote Sensing Image Power Tower Detection Algorithm Based on Multi-Scale Feature Extraction and Small Object Enhancement
by Ke Zhang, Yujie Cao, Chaojun Shi, Jiayi Li, Junchi Xiao, Liuyang Xue and Xun Deng
Appl. Sci. 2026, 16(15), 7462; https://doi.org/10.3390/app16157462 - 26 Jul 2026
Viewed by 371
Abstract
Satellite remote sensing has become a vital data source for monitoring power infrastructure. As critical infrastructure supporting power line operations, the monitoring and maintenance of power towers have become core elements in ensuring grid reliability. However, power tower detection in satellite remote sensing [...] Read more.
Satellite remote sensing has become a vital data source for monitoring power infrastructure. As critical infrastructure supporting power line operations, the monitoring and maintenance of power towers have become core elements in ensuring grid reliability. However, power tower detection in satellite remote sensing imagery remains challenging because of the substantial scale differences between distribution and transmission towers, the weak feature representation of small objects, and interference from complex backgrounds. To address these challenges, this paper proposes MS-YOLO, a power tower detection algorithm for satellite remote sensing imagery based on multi-scale feature extraction and small object enhancement. First, the poly kernel inception bottleneck (PKI_Bottleneck) module is introduced into the YOLOv9 backbone, enhancing the extraction of scale-diverse features and contextual cues while limiting interference from complex backgrounds. Second, the dual-branch semantic-spatial synergy attention (DSSA) module is introduced. By decoupling deep semantic and shallow spatial information, it effectively preserves small object features while suppressing environmental noise, enhancing the perception capability for small tower objects. Finally, a dynamic focal-weighted intersection over union (DFW-IoU) loss function is introduced to optimize the balance between easy and difficult samples, compelling the model to prioritize small objects and challenging samples during gradient updates. Experimental results demonstrate that MS-YOLO achieves mAP50 values of 79.1% and 95.7% on the two datasets used in this paper, representing improvements of 4.1% and 3.4% over baseline model. These results validate the effectiveness of the improved model for power tower detection in complex remote sensing scenarios. Full article
(This article belongs to the Special Issue AI in Object Detection—2nd Edition)
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