Object detection and recognition using contour based edge detection and fast R-CNN

Rani, Shilpa; Ghai, Deepika; Kumar, Sandeep

doi:10.1007/s11042-021-11446-2

Cited by 29 publications

(5 citation statements)

References 29 publications

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“…At present, Machine Learning (ML) sees growing utilization in controlling engine parameters [9], categorizing images [10], eliminating background noise [11], and identifying objects and edges [12]. Concerning the latter, the literature shows the promising results of deep learning algorithms based on a Convolutional Neural Network (CNN) [13,14].…”

Section: Introductionmentioning

confidence: 99%

Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach

Ricci,

Mariani

2024

Energies

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This research explores the detection of flame front evolution in spark-ignition engines using an innovative neural network, the autoencoder. High-speed camera images from an optical access engine were analyzed under different air excess coefficient λ conditions to evaluate the autoencoder’s performance. This study compared this new approach (AE) with an established method used by the same research group (BR) across multiple combustion cycles. Results revealed that the AE method outperformed the BR in accurately identifying flame pixels and significantly reducing overestimations outside the flame boundary. AE exhibited higher sensitivity levels, indicating its superior ability to identify pixels and minimize errors compared to the BR method. Additionally, AE’s accuracy in representing combustion evolution was notably improved, offering a more detailed depiction of the process. AE’s strength lies in its independence from specific threshold searches, a requirement in the BR method. By relying on learned representations within its latent space, AE eliminates laborious threshold exploration, ensuring reliability and reducing workload pressures. Comparative analyses consistently confirmed AE’s superior performance in accurately reproducing and delineating combustion evolution compared to BR. This study highlights AE’s potential as a promising technique for precise flame front detection in combustion processes. Its ability to autonomously extract features, minimize errors, and enhance overall accuracy signifies a significant step forward in analyzing flame fronts. AE’s reliability, reduced need for manual intervention, and adaptability across various conditions suggest a promising future for improving combustion analysis techniques in spark-ignition engines with optical access.

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Section: Introductionmentioning

confidence: 99%

Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach

Ricci,

Mariani

2024

Energies

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“…We evaluate each algorithm's strengths and weaknesses and provide insights into how they might be applied in different agricultural contexts [10]. The practical insights our study offers for improved resource allocation, agricultural management techniques, and resistance to climatic variability and market uncertainty have a substantial impact on agricultural stakeholders [11]. Our crop production forecast model is useful for enhancing agricultural decision-making and advancing sustainable growth in the agricultural sector.…”

Section: Introductionmentioning

confidence: 99%

Empowering Agriculture: Leveraging Intelligent Systems for Sustainable Farming

Sandeep Kumar, Monali Gulhane, Nitin Rakesh

2024

jes

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Climate change, market volatility, water scarcity, and pest control issues are just a few of the challenges that farmers face. Limited access to resources and education, as well as the slow adoption of new technologies, aggravate these problems. Creating intelligent systems is necessary to handle these pressing issues. This method makes use of key components such as crop type, weather, and soil properties to forecast agricultural yields. This system offers significant yield prediction capabilities for 53 crops by utilizing complex algorithms including XG-Boost, random forest, and decision tree. It takes into account crucial factors to accurately assess crop production potential, such as temperature, rainfall, nutrient levels (N, P, and K), soil pH, and temperature data. Cutting-edge machine learning techniques look at past data and trends to provide farmers with crucial information they need to make wise decisions. In order to solve the problems farmers, encounter in the modern agricultural sector, this intelligent system aims to enhance resource efficiency, farming systems' resilience and production, and the use of resources.

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“…MLP classifiers can be a better approach for predicting depression in humans using speech signals. They can learn essential elements from raw data, such as physiological signals or text, without relying on handcrafted features [8][9][10]. This is particularly relevant for depression prediction, where relevant features may not be immediately apparent or vary across individuals.…”

Section: Introductionmentioning

confidence: 99%

Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach

Narayanrao,

Kohirker,

Preeth

et al. 2024

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In the field of mental health diagnostics, the acoustic characteristics of speech have been recognized as potent markers for the identification of depressive symptoms. This study harnesses the power of transfer learning (TL) to discern depression-related sentiments from speech. Acoustic features such as rhythm, pitch, and tone form the core of this analysis. The methodology unfolds in three distinct phases. Initially, a Multi-Layer Perceptron (MLP) network employing stochastic gradient descent is applied to the RAVDESS dataset, yielding an accuracy of 65%. This finding catalyzes the second phase, wherein a comprehensive hyperparameter optimization via grid search (GS) is conducted on the MLP Classifier. This step primarily focuses on detecting emotions commonly associated with depression, including neutrality, sadness, anger, fear, and disgust. The optimized MLP classifier indicates an improved accuracy of 71%. In the final phase, to enhance precision further, the same GS-based model, underpinned by TL principles, is applied to the TASS dataset. This application astonishingly achieves an accuracy of 99.80%, suggesting a high risk of depression. This comparative study establishes the proposed framework as a vanguard in the application of TL for depression prediction, showcasing a significant leap in accuracy over previous methodologies.

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Object detection and recognition using contour based edge detection and fast R-CNN

Cited by 29 publications

References 29 publications

Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach

Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach

Empowering Agriculture: Leveraging Intelligent Systems for Sustainable Farming

Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach

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