Automatic Target Recognition and Identification for Military Ground-to-Air Observation Tasks using Support Vector Machine and Information Fusion

Sumari, Arwin Datumaya Wahyudi; Pranata, Aldi Surya; Mashudi, Irsyad Arif; Syamsiana, Ika Noer; Sereati, Catherine Olivia

doi:10.1109/iciss55894.2022.9915256

Cited by 4 publications

(1 citation statement)

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“…Some handcrafted image processing methods, such as histogram of oriented gradient [7] (HOG) and Deformable Part Model [8] (DPM), have demonstrated drawbacks, including poor robustness and sensitivity to scale variations. Sumari [9] employed Support Vector Machine (SVM) to recognize aerial military targets. They utilized SVM to learn the knowledge of 11 features, including the wing, engine, fuselage, and tail of military airplanes, achieving good accuracy on their datasets.…”

Section: Introductionmentioning

confidence: 99%

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Zeng,

Gao,

et al. 2024

Remote Sensing

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Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors.

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Section: Introductionmentioning

confidence: 99%

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Zeng,

Gao,

et al. 2024

Remote Sensing

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A Handy Simulated Radar Interface for Black Flight Identification System

Sumari,

Asmara,

Risman

et al. 2024

Smart Innovation, Systems and Technologies

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KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images

et al. 2023

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Aerial remote sensing image object detection, based on deep learning, is of great significance in geological resource exploration, urban traffic management, and military strategic information. To improve intractable problems in aerial remote sensing image, we propose a high-precision object detection method based on YOLOv5 for aerial remote sensing image. The object detection method is called KCFS-YOLOv5. To obtain the appropriate anchor box, we used the K-means++ algorithm to optimize the initial clustering points. To further enhance the feature extraction and fusion ability of the backbone network, we embedded the Coordinate Attention (CA) in the backbone network of YOLOv5 and introduced the Bidirectional Feature Pyramid Network (BiFPN) in the neck network of conventional YOLOv5. To improve the detection precision of tiny objects, we added a new tiny object detection head based on the conventional YOLOv5. To reduce the deviation between the predicted box and the ground truth box, we used the SIoU Loss function. Finally, we fused and adjusted the above improvement points and obtained high-precision detection method: KCFS-YOLOv5. This detection method was evaluated on three datasets (NWPU VHR-10, RSOD, and UCAS-AOD-CAR). The comparative experiment results demonstrate that our KCFS-YOLOv5 has the highest accuracy for the object detection in aerial remote sensing image.

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Automatic Target Recognition and Identification for Military Ground-to-Air Observation Tasks using Support Vector Machine and Information Fusion

Cited by 4 publications

References 0 publications

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

A Handy Simulated Radar Interface for Black Flight Identification System

KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images

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