A Dual Fine Grained Rotated Neural Network for Aerial Solar Panel Health Monitoring and Classification

Kar, Indra Narayan; Mukhopadhyay, S.; Guha, B. K.

doi:10.1007/978-981-99-1414-2_35

Cited by 1 publication

(1 citation statement)

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“…These tactics guarantee that just the most significant parts of a picture are analysed, allowing the algorithm to focus on areas critical for object localization and recognition. This enhances its capacity to handle complicated things, respond fast to visual cues, and function effectively in tough conditions [12]. The hybrid architecture's performance was evaluated using a variety of real-world datasets, such as interior settings, animal photos, and metropolitan landscapes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Object Detection Revolution: Deep Learning with Attention, Semantic Understanding, and Instance Segmentation for Real-World Precision

Shaik,

Banerjee,

Begum

et al. 2024

IJACSA

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Semantic and instance segmentation are critical goals that span a wide range of applications, from autonomous driving to object recognition in different fields. The existing approaches have limitations, especially when it comes to the difficult task of identifying and detecting minute things in intricate real-world situations. This work presents a novel method that uses a hybrid deep learning architecture with the Python programming language to smoothly combine semantic and instance segmentation. The suggested approach takes care of the pressing necessity in challenging real-world settings for accurate localization and fine-grained object detection. By combining the strengths of a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM), the hybrid model effectively achieves semantic segmentation by using sequential input and spatial information. A parallel attention method is smoothly included into the segmentation process to further improve the model's capabilities and enable the recognition of important object attributes. This study highlights the difficulties caused by changing environmental elements, highlighting the need for precise object location and understanding in addition to the complexities of fine-grained object detection. The suggested approach has an outstanding accuracy rate of 99.66%, outperforming existing approaches by 25.22%. This significant increase highlights the benefits that the hybrid design has over individual techniques and shows how effective it is at resolving issues that arise in dynamic real-world circumstances. The research highlights the importance of attention processes in deep learning and demonstrates how they might improve the specificity and accuracy of object detection and localization in intricate realworld scenarios. The improved performance of the suggested methodology is with well-known techniques like RCNN, CNN, and DNN, reaffirming its status as a reliable means of developing object localization and recognition in difficult situations.

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Section: Introductionmentioning

confidence: 99%