Research on food safety sampling inspection system based on deep learning

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

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TOWARDS ROBUST HUMAN MILLIMETER WAVE IMAGING INSPECTION SYSTEM IN REAL TIME WITH DEEP LEARNING

Progress In Electromagnetics Research ◽

10.2528/pier18012601 ◽

2018 ◽

Vol 161 ◽

pp. 87-100 ◽

Cited By ~ 3

Author(s):

Chenyu Liu ◽

Ming-Hui Yang ◽

Xiao-Wei Sun

Keyword(s):

Deep Learning ◽

Real Time ◽

Millimeter Wave ◽

Inspection System ◽

Millimeter Wave Imaging ◽

Wave Imaging

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Enhanced Food Safety Through Deep Learning for Food Recalls Prediction

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-030-61527-7_37 ◽

2020 ◽

pp. 566-580

Author(s):

Georgios Makridis ◽

Philip Mavrepis ◽

Dimosthenis Kyriazis ◽

Ioanna Polychronou ◽

Stathis Kaloudis

Keyword(s):

Deep Learning ◽

Food Safety ◽

Food Recalls

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Automated defect inspection system for metal surfaces based on deep learning and data augmentation

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2020.03.009 ◽

2020 ◽

Vol 55 ◽

pp. 317-324 ◽

Cited By ~ 5

Author(s):

Jong Pil Yun ◽

Woosang Crino Shin ◽

Gyogwon Koo ◽

Min Su Kim ◽

Chungki Lee ◽

...

Keyword(s):

Deep Learning ◽

Metal Surfaces ◽

Data Augmentation ◽

Inspection System ◽

Defect Inspection

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Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

Applied Sciences ◽

10.3390/app11178243 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8243

Author(s):

Jung-Sing Jwo ◽

Ching-Sheng Lin ◽

Cheng-Hsiung Lee ◽

Li Zhang ◽

Sin-Ming Huang

Keyword(s):

Deep Learning ◽

Intelligent System ◽

Working Hours ◽

Inspection System ◽

Practical Case ◽

Assembly Quality ◽

Press Fit ◽

Manual Inspection ◽

Testing Dataset ◽

Traditional Manufacturing

Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

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Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

Sensors and Materials ◽

10.18494/sam.2020.2921 ◽

2020 ◽

Vol 32 (10) ◽

pp. 3429

Author(s):

Chen-Chiung Hsieh ◽

Ya-Wen Lin ◽

Li-Hung Tsai ◽

Wei-Hsin Huang ◽

Shang-Lin Hsieh ◽

...

Keyword(s):

Deep Learning ◽

Inspection System

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MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques

10.5121/csit.2021.112402 ◽

2021 ◽

Author(s):

Sara Saleh Alfozan ◽

Mohamad Mahdi Hassan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Food Safety ◽

Early Detection ◽

Deep Neural Network ◽

High Accuracy ◽

Vital Role ◽

Mobile App ◽

Learning Techniques ◽

Agricultural Plants

Infection of agricultural plants is a serious threat to food safety. It can severely damage plants' yielding capacity. Farmers are the primary victims of this threat. Due to the advancement of AI, image-based intelligent apps can play a vital role in mitigating this threat by quick and early detection of plants infections. In this paper, we present a mobile app in this regard. We have developed MajraDoc to detect some common diseases in local agricultural plants. We have created a dataset of 10886 images for ten classes of plants diseases to train the deep neural network. The VGG-19 network model was modified and trained using transfer learning techniques. The model achieved high accuracy, and the application performed well in predicting all ten classes of infections.

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The circulation analysis of substandard foods in China based on GIS and social network analysis

PLoS ONE ◽

10.1371/journal.pone.0248037 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248037

Author(s):

Yakun He ◽

Jiadong Jiang ◽

Shuo Li

Keyword(s):

Social Network ◽

Food Safety ◽

Social Network Analysis ◽

Network Analysis ◽

Safety Issues ◽

Network Connection ◽

Sampling Inspection ◽

Safety Risks ◽

Province Level ◽

Circulation Characteristics

In China, the majority of food enterprises are small-sized and medium-sized. While the supervision costs are high, food safety issues are still emerging. Food circulation is an indispensable part in the entire food chain. At present, there are few studies on the regional spread of food safety risks in the circulation field from a macro perspective. This study combines GIS and social network analysis methods to deeply explore the regional circulation characteristics of substandard foods. First, we crawl the dataset of Food Safety Sampling Inspection Result Query System. Then we obtain the geographical locations of the manufacturers and distributors by GIS. Finally, we construct the province-level and city-level substandard foods’ circulation networks, and employ social network analysis to target key cities and paths. The experimental results show that the circulations of substandard foods are characterized by dense province-level network and sparse city-level network, and they are mostly local and short-distance trafficking. 361 cities are divided into 13 city clusters considering the network connection characteristics. Chongqing, Beijing, Zhengzhou, and Changsha are identified as key cities by all measurement indicators, and at least four indicators can identify Shanghai and Wuhan. These cities have the highest priority for combating substandard foods’ circulation networks.

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A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

Processes ◽

10.3390/pr9111895 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1895

Author(s):

Donggyun Im ◽

Sangkyu Lee ◽

Homin Lee ◽

Byungguan Yoon ◽

Fayoung So ◽

...

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Data Acquisition ◽

Type I Error ◽

Vision System ◽

Structural Characteristics ◽

Inspection System ◽

Type I ◽

Data Annotation ◽

Background Removal

Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator’s structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion.

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