An Enhanced Multi-Objective-Derived Adaptive DeepLabv3 Using G-RDA for Semantic Segmentation of Aerial Images

Anilkumar, Patel Hardik; Venugopal, P

doi:10.1007/s13369-023-07717-9

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Here, the performance of the Semantic segmentation of Aerial Images has been assimilated over the conventional models in terms of metrics like Accuracy and Dice Coefficient. The algorithms such as Butterfly Optimization Algorithm (BOA) [29], Coyote Optimization Algorithm (COA) [30], Glow-worm Swarm Optimization (GSO) [31], and classifiers like UNet [32], Deeplabv3 [33], MC-Dee-plabv3 [34] and G-RDA-Deeplabv3 [35] has been utilized for assimilation over AMC-Dee-pLabV3+ model. The maximum Iteration was 10; the chromosome length was 2 as well as the number of Populations was 10.…”

Section: Methodsmentioning

confidence: 99%

An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

P.,

2023

PLoS ONE

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Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has been designed to resolve the above-mentioned issues as well. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). Here, the hyperparameters of Multichannel deeplabv3+ are optimized by the IBWO algorithm. The proposed model significantly enhances the performance of the overall system by measuring the accuracy and dice coefficient. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively with a computation time of 113.0123 seconds. The evolutional outcomes of the proposed model show significantly better than the state of the art techniques like CNN, MUnet and DFCNN models.

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Section: Methodsmentioning

confidence: 99%

An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

P.,

2023

PLoS ONE

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“…Neural Networks, particularly deep learning models, have a proven track record of capturing complex and abstract patterns in data. Their ability to learn intricate relationships within the fused embeddings is crucial for accurate classification in medical diagnostics [47].…”

Section: Capability To Model Complex Patternsmentioning

confidence: 99%

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Bhushan Rajendra Nandwalkar

2024

anvi

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In the field of medical diagnosis, combining different types of information like text, images, and audio is a big step forward in making patient assessments more accurate. This research introduces an innovative method to bring together and categorize these different types of data. This method fills an important gap in current research [50, 54]. Proposed approach focuses on turning each type of data—text, images, and audio—into useful numbers. Text data is processed to extract meaning and context, while images are analysed using advanced computer techniques to capture important visual details. We also carefully examine audio data to extract important sound features, which is often overlooked but can be a valuable source of diagnostic information [48]. What makes our method special is how we combine these different types of data. We designed a strategy to blend these diverse sets of numbers into a single, enriched representation. This approach keeps the unique characteristics of each data type intact while harnessing their combined power for diagnosis [22, 29]. After combining the data, we use a well-chosen classification model that's known for its ability to make sense of complex data, especially in medical diagnosis scenarios [67, 71]. Proposed approach is rigorously assessing our method using a set of strong metrics that measure not only how accurate it is but also how reliable and valid it is for diagnosis [90, 94]. The results of this study mark a significant step forward in the field of combining different types of data, showing how it can greatly improve medical diagnosis. This method has the potential to revolutionize healthcare, enabling more precise and comprehensive data-driven decisions [143, 156].

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“…The generator [5,30,31] receives a multifaceted input comprising various components: an initial frame image, an intermediary frame image, a concluding frame image, and their corresponding labeled image. In this study, due to the relative displacement of the UAV when photographing the buildings, the external shape of the buildings does not change with its movement; therefore, we classify this 'building movement' as rigid motion, leading us to adopt an optical flow model with a uniform smoothing strategy.…”

Section: Semi-supervised Optical Flow Estimation Channel In Dual-chan...mentioning

confidence: 99%

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

Zhang,

Wu,

Man

et al. 2023

Remote Sensing

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Accurate building extraction holds paramount importance in various applications such as urbanization rate calculations, urban planning, and resource allocation. In response to the escalating demand for precise low-altitude unmanned aerial vehicle (UAV) building segmentation in intricate scenarios, this study introduces a semi-supervised methodology to alleviate the labor-intensive process of procuring pixel-level annotations. Within the framework of adversarial networks, we employ a dual-channel parallel generator strategy that amalgamates the morphology-driven optical flow estimation channel with an enhanced multilayer sensing Deeplabv3+ module. This approach aims to comprehensively capture both the morphological attributes and textural intricacies of buildings while mitigating the dependency on annotated data. To further enhance the network’s capability to discern building features, we introduce an adaptive attention mechanism via a feature fusion module. Additionally, we implement a composite loss function to augment the model’s sensitivity to building structures. Across two distinct low-altitude UAV datasets within the domain of UAV-based building segmentation, our proposed method achieves average mean pixel intersection-over-union (mIoU) ratios of 82.69% and 79.37%, respectively, with unlabeled data constituting 70% of the overall dataset. These outcomes signify noteworthy advancements compared with contemporaneous networks, underscoring the robustness of our approach in tackling intricate building segmentation challenges in the domain of UAV-based architectural analysis.

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An Enhanced Multi-Objective-Derived Adaptive DeepLabv3 Using G-RDA for Semantic Segmentation of Aerial Images

Cited by 5 publications

References 32 publications

An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

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