Improved recognition rate of different material category using convolutional neural networks

Shukla, Abhay; Kalnoor, Gauri; Kumar, Amit; Yuvaraj, N.; Manikandan, R.; Ramkumar, M.

doi:10.1016/j.matpr.2021.04.307

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Using material recognition for FOD classification becomes challenging as the features required for material properties of the items are not shape-dependent but depend on other properties like reflection properties, transparency, brightness, texture etc. The two main approaches to feature extraction for material recognition are handcrafted features and automatic feature extraction [5]. Most of the earlier works [20] involve handcrafted approaches but they are computationally expensive and slow processes, especially for sensitive applications like airport security.…”

Section: B Materials Recognition For Fod Detectionmentioning

confidence: 99%

“…Furthermore, in contrast to the decades of research on object recognition, material recognition is a flourishing and challenging field. The two main approaches followed by scientists for material recognition are handcrafted and automatic feature extraction as shown in Fig 1 . Hand-crafted feature extraction further divides into surface reflectance [3], 3D texture [4], and feature fusion [5] approaches. All these approaches involve the collection of features from images through approaches like bidirectional reflectance distribution function Bidirectional Reflectance Distribution Function (BRDF) [6], Scale Invariant Feature Transform (SIFT) [7], Histogram of Gradient (HOG) [8], interest points [9], optical pyramids or optical flow [10].…”

Section: Introductionmentioning

confidence: 99%

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Foreign Object Debris (FOD) Classification Through Material Recognition Using Deep Convolutional Neural Network With Focus on Metal

et al. 2023

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Foreign object debris (FOD) is any undesired and unintended object placed or found in the specific vicinity of an aircraft (runway/ taxiway) that can cause damage to aircraft or harm personnel on board such as twisted metal strips, screws, nuts, and bolts, depleted concrete runway pieces, stones, pebbles and stationery items. To avoid FOD damages, all airport/ aviation organizations have deployed some sort of FOD prevention procedure. However, automatic FOD detection systems are still scarce owing to the inevitable reliance on human experts that lead to unavoidable human errors. Around 60% of FOD consists of metal which is the most deteriorating for an aircraft. Therefore, the implementation of material recognition techniques for FOD classification through Deep Convolutional Neural Networks (DCNN) is more important than FOD object detection as FOD could be of any shape, size or color. This paper developed a DCNN algorithm for FOD material classification with high accuracy for all included material classes (i.e., metal, concrete, plastic) in general and metal in particular. For this, a new dataset is introduced that consists of 2481 images taken on an operational airport runway in varying illumination and weather conditions. Through extensive testing, it was found that InceptionV3 is the best performing model with 18% improvement in metal recognition, and 11% improvement in average accuracy for all included classes.

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Section: B Materials Recognition For Fod Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Foreign Object Debris (FOD) Classification Through Material Recognition Using Deep Convolutional Neural Network With Focus on Metal

et al. 2023

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“…CNNs are chosen for their success in material identification. 9 The architecture and training effort of the CNN may vary depending on the experimental parameters such as optical pulse width, temporal resolution, and detector efficiency. Each pixel's peak count is compared to its neighbors and patterns that form from the deviations become identifying characteristics.…”

Section: Data Processingmentioning

confidence: 99%

Material recognition using time of flight Lidar surface analysis

Tafone

McEnvoy²,

Sua³

et al. 2023

Quantum Sensing, Imaging, and Precision Metrology

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We are investigating a method for identifying materials from a distance, even when they are obscured, using a technique called Quantum Parametric Mode Sorting and single photons detection. By scanning a segment of the material, we are able to capture data on the relationships between the peak count of photons reflected at each position and the location of that reflection. This information allows us to measure the relative reflectance of the material and the texture of its surface, which enables us to achieve a material recognition accuracy of 99%, even maintaining 89.17% when materials are obscured by a lossy and multi-scattering obscurant that causes up to 15.2 round-trip optical depth.

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“…Supply chain (SC) networks have evolved to establish a robust framework for conducting business in unforeseeably challenging circumstances, such as when suppliers are unable to meet customer demands. These Resilient Supply Chain (RSC) networks were instituted to provide a stable foundation for business operations in unpredictable and challenging environments (Shukla et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Supply Chain Management of Drug Products in Blockchain Using Reinforcement Learning

Alla,

Thangarasu

2023

IJTech

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Supply chain management (SCM) is a complex system that consists of two parts: a management system for the medication supply chain and a recommendation system. The first part of the system is the management system powered by reinforcement learning. The reinforcement Learning model is trained to recommend the most suitable medications for a diverse set of customers. The model will be fed customer ratings and reviews via the proposed framework client application, where it will learn over time to make the most informed drug recommendations possible. The proposed system will provide patients with recommendations for successful drug therapies.

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Improved recognition rate of different material category using convolutional neural networks

Cited by 9 publications

References 23 publications

Foreign Object Debris (FOD) Classification Through Material Recognition Using Deep Convolutional Neural Network With Focus on Metal

Foreign Object Debris (FOD) Classification Through Material Recognition Using Deep Convolutional Neural Network With Focus on Metal

Material recognition using time of flight Lidar surface analysis

Supply Chain Management of Drug Products in Blockchain Using Reinforcement Learning

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