A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat, Mahdi; Akbari, Leila; Xin, Qin

doi:10.1109/access.2019.2924445

Cited by 32 publications

(14 citation statements)

References 60 publications

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“…There are a lot of classical approaches for classification problems such as Random Forest (RF), AdaBoost, k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) [33][34][35][36][37][38] but to verify the object detection output accuratelly, we propose use of a pre-trained CNN as the classifier. The authors in [17] presented MobileNet as a class of more efficient models for mobile and embedded vision applications.…”

Section: The Verification Step Based On Deep Learning Classifiersmentioning

confidence: 99%

Wind turbine tower detection using feature descriptors and deep learning

Abedini

Bahaghighat²,

Shoyan

2020

Facta Univ Electron Energ

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Wind Turbine Towers (WTTs) are the main structures of wind farms. They are costly devices that must be thoroughly inspected according to maintenance plans. Today, existence of machine vision techniques along with unmanned aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection of the structures. Our work is aimed towards developing a visionbased system to perform Nondestructive tests (NDTs) for wind turbines using UAVs. In order to navigate the flying machine toward the wind turbine tower and reliably land on it, the exact position of the wind turbine and its tower must be detected. We employ several strong computer vision approaches such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then, in order to increase the reliability of the system, we apply the ResNet, MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order to verify whether a detected object is indeed a turbine tower or not.

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Section: The Verification Step Based On Deep Learning Classifiersmentioning

confidence: 99%

Wind turbine tower detection using feature descriptors and deep learning

Abedini

Bahaghighat²,

Shoyan

2020

Facta Univ Electron Energ

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“…In order to increase the lifetime of WTs and reduce the maintenance cost, it is essential to have accurate prediction of system faults and failures using advanced Non Destructive Tests (NDTs) methods during in-service operation [31]. These systems usually benefits smart sensors with artificial intelligence and machine learning techniques to perform advance inspections [5,[32][33][34][35][36].…”

Section: Message Type Example Application Time Constraintmentioning

confidence: 99%

“…There are a lot of image processing approaches to enhance quality of images and videos [32,[86][87][88][89][90] but at the first, its metric should be defined. Now, well-known metrics for assessing the quality of images and videos are VQM (Video quality measurement), PVQM (Perceptual video quality measure), MPQM (Moving picture quality metrics), SSIM (Structural Similarity Index), MSSIM (Mean SSIM), FSIM (feature-similarity), PSNR (Maximum signal to noise ratio), and HVS (human visual system) [85,[91][92][93].…”

Section: Performance Evaluation Metric For the Proposed Image Transmimentioning

confidence: 99%

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

2019

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Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.

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“…AOI has been increasingly used for automatic defect detection in several industrial fields for the quality control of products, such as printed circuit boards [1]- [3], plastic products [4], steel products [5], [6], glass products [7], [8], solar cells [9], [10], textiles and garments [11]. Besides, AOI has also been used for counting blister cards within drug packages [12] and checking the surface of the lithium-ion battery electrode [13].…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for Optical Film Defect Detection and Classification

Wang

Shih

et al. 2020

IEEE Access

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Currently, liquid crystal displays (LCDs) are the most popular type of flat panel display and are used in most applications. An LCD contains many critical optical film components that are produced in highly automated and precisely monitored facilities throughout the complex manufacturing process. However, defect detection and classification through visual inspection is very difficult during the manufacturing process. To overcome this problem, a novel framework based on machine vision known as the optical film defect detection and classification system is presented for use in the real-time inspection. First, an image acquisition system equipped with a high-resolution camera and custom-made lighting field was designed to obtain a high-quality optical film image. Second, the defects on an optical film were detected using localized cross-projection based on proposed adaptive energy analysis. Finally, the defect images were classified into four types by using the developed classification algorith-point, scratch, foreign material, and stain. The quality of the products yielded after defect detection and classification of the optical film was compared with the standard product quality of the manufacturer. Experiments were conducted using samples collected from the largest manufacturer in Taiwan to validate the performance of the proposed framework. The accurate defect detection rate is 99.6%, the classification accuracy rate is 100%, and the total operation time is short whichonly 6.129s are required on average to perform the inspection for an optical film sample. The results demonstrate that the proposed method is sound and useful for optical film inspection in industries.

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A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Cited by 32 publications

References 60 publications

Wind turbine tower detection using feature descriptors and deep learning

Wind turbine tower detection using feature descriptors and deep learning

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

Novel Framework for Optical Film Defect Detection and Classification

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