Department of Electrical and Electronics Engineering

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Author: search.filters.author.Narang, PratikHas files: No

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    AGD-Net: Attention-Guided Dense Inception U-Net for Single-Image Dehazing
    (Springer, 2023-12) Chamola, Vinay; Narang, Pratik
    Image hazing poses a significant challenge in various computer vision applications, degrading the visual quality and reducing the perceptual clarity of captured scenes. The proposed AGD-Net utilizes a U-Net style architecture with an Attention-Guided Dense Inception encoder-decoder framework. Unlike existing methods that heavily rely on synthetic datasets which are based on CARLA simulation, our model is trained and evaluated exclusively on realistic data, enabling its effectiveness and reliability in practical scenarios. The key innovation of AGD-Net lies in its attention-guided mechanism, which empowers the network to focus on crucial information within hazy images and effectively suppress artifacts during the dehazing process. The dense inception modules further advance the representation capabilities of the model, facilitating the extraction of intricate features from the input images. To assess the performance of AGD-Net, a detailed experimental analysis is conducted on four benchmark haze datasets. The results show that AGD-Net significantly outperforms the state-of-the-art methods in terms of PSNR and SSIM. Moreover, a visual comparison of the dehazing results further validates the superior performance gains achieved by AGD-Net over other methods. By leveraging realistic data exclusively, AGD-Net overcomes the limitations associated with synthetic datasets which are based on CARLA simulation, ensuring its adaptability and effectiveness in real-world circumstances. The proposed AGD-Net offers a robust and reliable solution for single-image dehazing, presenting a significant advancement over existing methods.
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    Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust
    (Springer Nature, 2024) Chamola, Vinay; Narang, Pratik; Rallapall, Srinivas
    This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervasive and destructive disease that significantly impacts wheat production worldwide. Traditional diagnostic methods often require expert knowledge and time-consuming processes, making rapid and accurate detection challenging. We drafted a new, WheatRustDL2024 dataset (7998 images of healthy and infected leaves) specifically designed for VQA in the context of wheat rust detection and utilized it to retrieve the initial weights on the federated learning server. This dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections. Our dataset also contains images collected from various sources and successfully highlights a wide range of conditions (different lighting, obstructions in the image, etc.) in which a wheat image may be taken, therefore making a generalized universally applicable model. The trained model was federated using Flower. Following extensive analysis, the chosen central model was ResNet. Our fine-tuned ResNet achieved an accuracy of 97.69% on the existing data. We also implemented the BLIP (Bootstrapping Language-Image Pre-training) methods that enable the model to understand complex visual and textual inputs, thereby improving the accuracy and relevance of the generated answers. The dual attention mechanism, combined with BLIP techniques, allows the model to simultaneously focus on relevant image regions and pertinent parts of the questions. We also created a custom dataset (WheatRustVQA) with our augmented dataset containing 1800 augmented images and their associated question-answer pairs. The model fetches an answer with an average BLEU score of 0.6235 on our testing partition of the dataset. This federated model is lightweight and can be seamlessly integrated into mobile phones, drones, etc. without any hardware requirement. Our results indicate that integrating deep learning with VQA for agricultural disease diagnostics not only accelerates the detection process but also reduces dependency on human experts, making it a valuable tool for farmers and agricultural professionals. This approach holds promise for broader applications in plant pathology and precision agriculture and can consequently address food security issues.
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    Low-Light Image Enhancement for UAVs With Multi-Feature Fusion Deep Neural Networks
    (IEEE, 2022-06) Narang, Pratik; Chamola, Vinay
    Object detection in low-light aerial images is a challenging problem due to considerable variation in brightness and varying contrast. Deep learning-based approaches have recently demonstrated great promise in image enhancement. Many existing neural networks used for image quality enhancement first encode the input into low-resolution representations and then decode these representations back to a higher resolution for the contextual information. However, this method leads to the loss of semantic content. Recent research has demonstrated the advantage of maintaining high-resolution information along with lower resolution representations, which maintains image features throughout the network. In this letter, we propose a novel architecture named RNet for low-light image enhancement of aerial images. The proposed network contains multiresolution branches for better understanding of different levels of local and global context through different streams. The performance of RNet is evaluated on a recent synthetic dataset. We also present a comprehensive evaluation with a representative set of state-of-the-art enhancement techniques and neural net architectures.
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    DroneSegNet: Robust Aerial Semantic Segmentation for UAV-Based IoT Applications
    (IEEE, 2022-04) Narang, Pratik; Chamola, Vinay
    Unmanned Aerial Vehicles (UAVs) are the promising “Flying IoT” devices of the future, which can be equipped with various sensors and cognitive capabilities to perform numerous tasks related to remote sensing, search and rescue operations, object tracking, segmentation of roads and buildings, surveillance, etc. However, these AI-driven tasks require heavy computation and may lead to suboptimal performance with embedded processors on a power-constrained battery-operated drone. This work proposes a novel deep learning approach for performing robust semantic segmentation of aerial scenes captured by UAVs. In our setup, the power-constrained drone is used only for data collection, while the computationally intensive tasks are offloaded to a GPU cloud server. Our architecture performs robust semantic segmentation by learning the segmentation maps from jointly utilizing of aerial scenes along with the respective “elevation maps” in a semi-supervised approach. We propose a three-tier deep learning architecture, wherein the first module aims at preliminary feature extraction from aerial scenes using a backbone feature extractor. The second module captures the spatial dependency between the aerial scenes and their respective elevation maps to obtain better semantic information, which is achieved by a bi-directional LSTM. The third module is aimed at enhancing the performance of semantic segmentation through a semi-supervised approach with an encoder to generate segmentation maps and a decoder to reconstruct feature maps. This semi-supervised feature learning ensures robust extraction along with scalability. The proposed architecture was validated on real-world aerial datasets and achieves state-of-the-art results for aerial image segmentation.