CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Defense Technology

Background:

• Convolutional Neural Networks (CNNs) are leading models for image classification but typically require extensive labeled datasets.
• Large-scale datasets are often unavailable for specialized applications like infrared (IR) imaging in defense.
• Robustness, particularly viewpoint invariance, is a critical challenge in real-world object recognition.

Purpose of the Study:

• To develop an effective object identification and recognition method for IR imaging in defense applications.
• To address the challenge of limited groundtruthed data for training deep learning models.
• To enhance the robustness of CNNs, especially against variations in object viewpoint.

Main Methods:

• Introduction of a compact, fully convolutional CNN architecture.
• Utilization of global average pooling within the CNN.
• Training the model using realistic simulation datasets.

Main Results:

• The proposed CNN achieved state-of-the-art performance compared to other CNNs.
• The model demonstrated strong performance without requiring data augmentation or fine-tuning.
• Significant improvements in robustness to viewpoint changes were observed compared to a Support Vector Machine (SVM) scheme.

Conclusions:

• A compact CNN with global average pooling is effective for IR object recognition in data-scarce defense scenarios.
• The proposed method offers a robust solution for object identification in the wild.
• Simulation-based training provides a viable pathway to achieve high performance in specialized imaging domains.