X‐ray computed tomography for quality inspection of agricultural products: A review

Du, Zhe; Hu, Yongguang; Buttar, Noman Ali; Mahmood, Ashraf

doi:10.1002/fsn3.1179

Cited by 62 publications

(29 citation statements)

References 96 publications

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“…The non-destructive study of products is an important topic for the food industry. It is applied to a variety of food samples [ 8 ], and every type of object has different details and typical defects to detect. X-ray imaging can be used for the detection of grain infection, fruit infestation, and bone detection in the fish and poultry industry.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

et al. 2021

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X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

et al. 2021

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“…(3) here is the digital signal from the detector with switched off X-ray source (dark signals), and is the digital signal from the detector without a test object. For an X-ray source with energy spectrum , where is the maximum radiation energy, and a radiation-sensitive element (RSE) of thickness operating in integrated recording mode, the analog signals (AS) that correspond to the digital signal are estimated using a formula similar to the expression from [25], (4) where is the factor of conversion of the absorbed radiation energy into the analog signal; is the number of photons hitting the front surface of the radiation-sensitive element without a test object during projection formation;…”

Section: Geometric Scheme Of Projection Formationmentioning

confidence: 99%

“…is the average value of energy transmitted to the radiation-sensitive element by a recorded photon with energy and is the efficiency of recording. In formula (4) is the projection for photons with energy when the informative parameter is the linear attenuation coefficient of radiation. For a radiation-sensitive element with small dimensions, it is necessary to take into account the leakage and transfer of energy to the environment and to neighboring elements.…”

Section: Geometric Scheme Of Projection Formationmentioning

confidence: 99%

“…In recent decades, there has been a steady increase in interest in X-ray computed tomography (CT) as a method for studying the structure of various test objects with steel dimensions from fractions of millimeters to hundreds of millimeters [1][2][3][4]. Prospects for the development of computed tomography are currently associated with measuring linear dimensions, areas, volumes, density, porosity, etc.…”

Section: Introductionmentioning

confidence: 99%

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Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Osipov

Yadrenkin

Чахлов

et al. 2021

Russ J Nondestruct Test

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Abstract— A calculation model of X-ray computed tomography with a density assessment function in the geometry of a parallel beam has been proposed. The model includes blocks for simulating and correcting sinograms and reconstructing section images. When generating sinograms, the parameters of the test object, source, and recorder of X-ray radiation have been taken into account. Modeling algorithms are implemented in the MathCad system and tested on virtual test objects.

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“…Depending on factors of interest, such as the material and hardness of the subject, various types of images, such as CT images or X-ray radiographic images, can be used to analyze the subject. Various studies, such as medicine, agriculture, and manufacturing, are actively being conducted [25][26][27][28][29][30][31]. These images have the advantage of seeing changes in parameters of interest (e.g., materials constituting the object, hardness, and height) from a photograph of the object at a glance [32].…”

Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

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X‐ray computed tomography for quality inspection of agricultural products: A review

Cited by 62 publications

References 96 publications

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

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