Automatic surface area and volume prediction on ellipsoidal ham using deep learning

Liong, Sze‐Teng; Gan, Yee Siang; Huang, Yen‐Chang

doi:10.1111/jfpe.13093

Cited by 16 publications

(8 citation statements)

References 33 publications

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“…To collect a sufficient amount of data to enable the proposed method validation, each cucumber is located at three different roller bar positions. Inspired by Gan et al (2021) and Liong et al (2019), the target object is self‐rotated for 360° at a fixed controlled speed to maximize data consistency. Thus, each cucumber is self‐rotated at 360° and the total amount of images collected at a fixed position is 22.…”

Section: Proposed Methodsmentioning

confidence: 99%

An automated cucumber inspection system based on neural network

Gan

Luo

et al. 2022

J Food Process Engineering

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Precision agriculture and smart farming have been gaining importance in recent years due to the coupled breakthrough of deep learning algorithms in machine vision. This paper aims to develop an end‐to‐end automatic agricultural food grading system based on its visual appearance. The target object considered herein is cucumber as it is one of the vegetables that can be grown in many countries around the world. Particularly, the developed system incorporates both the software and hardware components, in which the geometric properties of a moving cucumber on a conveyor belt can be computed. Concretely, an industrial camera is employed to capture the image of a cucumber. Then, three individual detection systems that perform the cucumber identification, geometry properties approximation, and defect detection, are designed. Finally, if the cucumber is found defective, the PLC motor control will be activated to separate the cucumber into an alternative container. As a result, the proposed algorithms yield promising performances when experimenting on a self‐collected data set, namely “Cuc‐70” that consists of a total of 4620 images. The cucumber identification generates an average WIoU of 93%, volume approximation accuracy of 98%, and defect detection WIoU of 92%. In addition, comprehensive analysis is conducted in order to validate the robustness of the proposed system and the compelling performance executed can be evidenced from the quantitative and qualitative results reported. In the future, this system can be integrated into online automatic sorting and grading for effective manufacturing and production.

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Section: Proposed Methodsmentioning

confidence: 99%

An automated cucumber inspection system based on neural network

Gan

Luo

et al. 2022

J Food Process Engineering

Self Cite

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“…Recently, Gan et al (2021) and Liong, Gan, and Huang (2019) intent to estimate the geometry properties (i.e., the surface area and volume) of an ellipsoidal ham object from an image. A series of image processing techniques that involve complex mathematical modelings and derivations demonstrate promising prediction performance exhibited by the proposed mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…The images exploited herein are directly obtained from the dataset released by Gan et al (2021) and Liong et al (2019). Note that each ham object has an irregular shape.…”

Section: Data Pre‐processingmentioning

confidence: 99%

Find the centroid: A vision‐based approach for optimal object grasping

Liong

Tan

et al. 2021

J Food Process Engineering

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Recently, the booming development of deep learning techniques facilitates remarkable progress in a wide range of applications, has provided insightful cues in today's modern industry and research societies. For instance, the manufacturing and agricultural industries have witnessed the unparallel dominance in AI vision to integrate the production management. Therefore, owing to its impressive learning capability, this article aims to develop an automated system that can integrate data processing and packaging operations. Notably, the inspection system can capture the geometric properties of an ellipsoidal ham and thus can be grasped by the robotic arm. Prior to the centroid detection, a series of instant segmentation procedures is introduced to determine the position of the object, which incorporates both the object localization and semantic segmentation processes. As a result, the proposed model yields a promising segmentation accuracy when experimented on the ham dataset that consists of 293 images. The best Sørensen–Dice coefficient achieved is 91%, which indicates the compelling performance. To further verify the effectiveness of the proposed framework, quantitative and qualitative visualization are presented to demonstrate the quality of the image segmentation task. The proposed algorithm and the raw data are publicly available at https://github.com/jason2303779/Ham-Centroid. Practical Applications Recently, the booming development of deep learning techniques facilitates remarkable progress in a wide range of applications has provided insightful cues in today's modern industry and research societies. For instance, the manufacturing and agricultural industries have witnessed the unparallel dominance in AI vision to integrate production management. Therefore, this paper aims to develop an automated system that can integrate data processing and packaging operations due to its impressive learning capability. In brief, this paper introduces the first plausible mechanism to precisely identify the geometric center of an irregular‐shaped object, viz, ellipsoidal ham. A novel pipeline that composes both the object detection and semantic segmentation architectures is proposed to facilitate the vision understanding process. Succinctly, the inspection system can capture an ellipsoidal ham's geometry properties and thus facilitate the robotics vision for food products, especially in stimulating automatic industrial manufacturing development.

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“…Succinctly, Mask R-CNN is built based on a feature pyramid network (FPN) [20] and a ResNet-101 [21] backbone architecture. In addition, Mask R-CNN had been achieving superior performance for the instance segmentation of biomedical images [22] (to predict the bounding boxes of the individual nuclei from a clustered nuclei), food and products [23] (to detect the ham object location and its boundary's coordinate), traffic understanding [24] (to detect and localize anomalies in traffic scenes), and others. and (d) automatic building segmentation using mask regional convolutional neural network (R-CNN) approach.…”

Section: Image Preprocessingmentioning

confidence: 99%

Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification

Huang

Wang

Liong

et al. 2020

IJCIS

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Architecture is about evolution, there exist many types of architectural styles that depend on the geography, traditions, and culture of the particular regions. An architectural designer may have a similar preference in creating the new architectural building, which can be easily recognized from the physical attributes and characteristics. This paper performs an architect classification based on the outward appearance of the building. An architecture database with 100 images (ARC-100) that have balanced class distribution is constructed. Among the architectural buildings, the best performance is 71% for 5-class classification. Convolutional neural networks (CNNs) have demonstrated breakthrough performance on various classification tasks in recent studies, and even outperform human experts in specific tasks. Thus, for the baseline evaluation, multiple pretrained CNN models are employed with slight modifications. Prior to the feature extraction and classification processes, the removal of background noise is performed using two approaches: manually and automatically. The former approach requires high human intervention, while the latter utilizes the cutting-edge object segmentation technology, namely mask regional convolutional neural network (R-CNN). The illustration of the experiment training progress and the confusion matrix are reported, to allow further interpretation and analysis for the model trained. Notably, this is the first work that performs automatic classification based on architectural styles. This framework can be used to improve the cultural understanding and practices in providing education for holistic development and enhance the learning experience and progressions from an aesthetic perspective.

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Automatic surface area and volume prediction on ellipsoidal ham using deep learning

Cited by 16 publications

References 33 publications

An automated cucumber inspection system based on neural network

An automated cucumber inspection system based on neural network

Find the centroid: A vision‐based approach for optimal object grasping

Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification

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