An Introduction to Deep Learning‐Based Object Recognition and Tracking for Enabling Defense Applications DOI

Nitish Mahajan,

Aditi Chauhan,

Monika Kajal

et al.

Published: Feb. 7, 2024

Object monitoring and surveillance technologies are crucial in defense, border protection, counter-terrorism operations. These enable military security personnel to monitor track the movement of objects individuals high-risk areas, detect potential threats, respond effectively intrusions or attacks. In object used see troop movements, enemy activities, provide real-time intelligence commanders. include radar systems, unmanned aerial vehicles (UAVs), satellite imagery. By providing early warning movements these help quickly effectively, increasing their chances success. illegal crossings, drug trafficking, smuggling activities. thermal imaging cameras, ground sensors, UAVs. information about control apprehend reducing risk incursions other threats. operations, threats prevent terrorist facial recognition biometric scanners, advanced systems. identifying dangers before they can carry out attacks, activities safeguard public. conclusion, critical enabling national protect citizens from harm.

Language: Английский

MODD-λ: Military Object Detection Dataset for Land-Air Integration and Cross-Domain Collaborative Unmanned Swarm Systems DOI

Yuichiro Hei,

Xuting Duan,

Xiaolong Yang

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 590 - 598

Published: Jan. 1, 2025

Language: Английский

Citations

0
Combining data augmentation and model fine-tuning for learning from limited data DOI
Hongbo Shi, Ying Zhang, Bowen Wan

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Language: Английский

Citations

0
Capsule networks for computer vision applications: a comprehensive review DOI
Seema Choudhary, Sumeet Saurav, Ravi Saini

et al.

Applied Intelligence, Journal Year: 2023, Volume and Issue: 53(19), P. 21799 - 21826

Published: June 14, 2023

Language: Английский

Citations

10
Development and implementation of a robotic moving target system for object recognition testing DOI
Haojie Zhang, Rung‐Huei Liang, Bo Zhao

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 146, P. 110223 - 110223

Published: Feb. 13, 2025

Language: Английский

Citations

0
Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis DOI Creative Commons
Rezaul Haque, Muhammad Ali Khan, Hameedur Rahman

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110166 - 110166

Published: April 17, 2025

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets do not combine different feature extraction techniques better classification. To address these challenges, we propose a robust explainable stacking ensemble model multiclass tumor that combines EfficientNetB0, MobileNetV2, GoogleNet, Multi-level CapsuleNet, using CatBoost as the meta-learner improved aggregation classification accuracy. This approach captures complex characteristics while enhancing robustness The proposed integrates CapsuleNet within framework, utilizing to improve We created two large by merging data from four sources: BraTS, Msoud, Br35H, SARTAJ. tackle applied Borderline-SMOTE augmentation. also utilized methods, along with PCA Gray Wolf Optimization (GWO). Our was validated through confidence interval analysis statistical tests, demonstrating superior performance. Error revealed misclassification trends, assessed computational efficiency regarding inference speed resource usage. achieved 97.81% F1 score 98.75% PR AUC M1, 98.32% 99.34% M2. Moreover, consistently surpassed state-of-the-art CNNs, Vision Transformers, other methods classifying across individual datasets. Finally, developed web-based diagnostic tool enables clinicians interact visualize decision-critical regions scans Explainable Artificial Intelligence (XAI). study connects high-performing AI real clinical applications, providing reliable, scalable, efficient solution

Language: Английский

Citations

0
A Lightweight Transformer Model for Real-Time Object Detection on Edge Devices in Surveillance Camera Systems DOI
Dung Nguyen, Van-Dung Hoang, Van-Tuong-Lan Le

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 221 - 234

Published: Jan. 1, 2025

Language: Английский

Citations

0
Identifying Camouflaged Objects Using Modified Picture Fuzzy Clustering DOI

A. Shokeen,

Manpreet Kaur, Trasha Gupta

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 45 - 57

Published: Jan. 1, 2025

Language: Английский

Citations

0
Multi-Scale Infrared Military Target Detection Based on 3X-FPN Feature Fusion Network DOI Creative Commons
Shuai Wang, Yuhong Du,

Shuaijie Zhao

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 141585 - 141597

Published: Jan. 1, 2023

To solve the problems of misdetection and omission infrared military targets poor detection effect in battlefield environments, an improved YOLOv4 algorithm is proposed to improve accuracy long-range target detection. First, a new 4th-scale feature extraction layer introduced enhance multi-scale sensitivity for targets. Second, TL intermediate channel realize fusion across gradient connections, 3X-FPN network structure proposed, adaptive parameters are adopted weighted balanced data accuracy. Finally, loss function established optimized model stability convergence effect. The depth separable convolution model's lightweight. experimental results vehicle class ablation show that increases by 9.85% compared with original algorithm, reduces volume 36%, its distance up 2000 m. achieves mean average precision (mAP) value 93.25% multi-military detection, which improves 12.42% mainstream meets current combat acquisition processing requirements.

Language: Английский

Citations

9
Military Vehicle Object Detection Based on Hierarchical Feature Representation and Refined Localization DOI Creative Commons
Yan Ouyang, Xinqing Wang, Ruizhe Hu

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 99897 - 99908

Published: Jan. 1, 2022

Military vehicle object detection technology in complex environments is the basis for implementation of reconnaissance and tracking tasks weapons equipment, great significance information intelligent combat. In response to poor performance traditional algorithms military detection, we propose a method based on hierarchical feature representation reinforcement learning refinement localization, referred as MVODM. First, task, construct reliable dataset MVD. Second, design two strategies, learning-based improve detector. The strategy can help detector select layer suitable scale, localization accuracy boxes. combination these strategies effectively Finally, experimental results homemade show that our proposed MVODM has excellent better accomplish task vehicles.

Language: Английский

Citations

13
Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar DOI Creative Commons

Yuanxue Ding,

Yanchen Qu,

Jianfeng Sun

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(15), P. 3553 - 3553

Published: July 24, 2022

Long-distance multi-vehicle detection at night is critical in military operations. Due to insufficient light night, the visual features of vehicles are difficult distinguish, and many missed detections occur. This paper proposes a two-level method for long-distance nighttime multi-vehicles based on Gm-APD lidar intensity images point cloud data. The divided into two levels. first level 2D detection, which enhances local contrast image improves brightness weak small objects. With confidence threshold set, result greater than reserved as reliable object, less suspicious object. In second 3D recognition, object area from converted corresponding classification judgment, score obtained through comprehensive judgment. Finally, results recognition merged final result. Experimental show that achieves accuracy 96.38% can effectively improve multiple better current state-of-the-art methods.

Language: Английский

Citations

12