Mr. Rohan Putatunda Profile

Rohan Putatunda

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Proceedings Article | 14 June 2023 Poster + Paper

Quasi-synthetic data generation for camouflaged object detection at edge

Rohan Putatunda, Md Azim Khan, Aryya Gangopadhyay, Jianwu Wang, Robert F. Erbacher

Proceedings Volume 12529, 1252916 (2023) https://doi.org/10.1117/12.2678034

KEYWORDS: Data modeling, Object detection, Education and training, Performance modeling, Image processing, Light sources and illumination, Instrument modeling, Displays, RGB color model, Camouflage

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Detecting camouflaged objects is crucial in various applications such as military surveillance, wildlife conservation, and in search and rescue operations. However, the limited availability of camouflaged object data poses a significant challenge in developing accurate detection models. This paper proposes a quasi-synthetic data generation by image compositing combined with attention-based deep learning-based harmonization methodology to generate feature-enriched realistic images for camouflaged objects under varying scenarios. In our work, we developed a diverse set of images to simulate different environmental conditions, including lighting, shadows, fog, dust, and snow, to test our proposed methodology. The intention of generating such photo-realistic images is to increase the robustness of the model with the additional benefit of data augmentation for training our camouflaged object detection model(COD). Furthermore, we evaluate our approach using state-of-the-art object detection models and demonstrate that training with our quasi-synthetic images can significantly improve the detection accuracy of camouflaged objects under varying conditions. Additionally, to test the real operational performance of the developed models, we deployed the models on resource-constrained edge devices for real-time object detection to validate the performance of the trained model on quasi-synthetic data compared to the synthetic data generated by conventional neural style transfer architecture.

Proceedings Article | 31 May 2022 Presentation + Paper

Camouflaged object detection system at the edge

Rohan Putatunda, Aryya Gangopadhyay, Robert Erbacher, Carl Busart

Proceedings Volume 12096, 120960I (2022) https://doi.org/10.1117/12.2618869

KEYWORDS: Performance modeling, Camouflage, Image segmentation, Data modeling, Systems modeling, Instrument modeling, Head, Fluctuations and noise, Visualization

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Camouflage is the art of deception which is often used in the animal world. It is also used on the battlefield to hide military assets. Camouflaged objects hide within their environments by taking on colors and textures that are similar to their surroundings. In this work, we explore the classification and localization of camouflaged enemy assets including soldiers. In this paper we address two major challenges: a) how to overcome the paucity of domain-specific labeled data and b) how to perform camouflage object detection using edge devices. To address the first challenge, we develop a deep neural style transfer model that blends content images of objects such as soldiers, tanks, and mines/improvised explosive devices with style images depicting deserts, jungles, and snow-covered regions. To address the second challenge, we develop combined depth-guided deep neural network models that combine image features with depth features. Previous research suggests that depth features not only contain local information about object geometry but also provide information on the position, and shape for camouflaged object identification and localization. In this work, we use precomputed monocular method for the generation of the depth maps. The novel fusion-based architecture provides an efficient representation learning space for object detection. In addition, we perform ablation studies to measure the performance of depth versus RGB in detecting camouflaged objects. We also demonstrate how such as model can be deployed in edge devices for real-time object identification and localization.

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