RETRACTED ARTICLE: Research on path guidance of logistics transport vehicle based on image recognition and image processing in port area

Jiang, Lingyong; Fan, Yongjiao; Sheng, Qingxin; Feng, Xia‐Ting; Wang, Wei

doi:10.1186/s13640-018-0384-5

Cited by 5 publications

(3 citation statements)

References 16 publications

(14 reference statements)

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“…In other words, the application of image recognition and DL is becoming a concept within the field of logistics and supply chain. For instance, image recognition can better facilitate logistics and transportation and solve the errors in many fully automated transport vehicles due to large-scale track identification errors [3]. Another application of DL is the classification of fruits.…”

Section: Introductionmentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

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Section: Introductionmentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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“…According to the surrounding environment, additional information, and other means, path optimization has adjusted dynamically when the system running. 14,15 The change of surrounding environment, information, and operating mode are the major factors to affect the path chosen in logistics robot and autonomous vehicles. 16,17 Drug circulation in pharmacy is a key link of modern pharmaceutical logistics that lead to medical logistic robots developed for reducing dispensing errors, including comparability misses of drugs' name and dispensing dysfunction, which occur at this stage may harm the patient directly.…”

Section: Introductionmentioning

confidence: 99%

Optimization for medical logistics robot based on model of traveling salesman problems and vehicle routing problems

Jin

et al. 2021

International Journal of Advanced Robotic Systems

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Fast medicine dispensing system (FMDS) as a kind of medical logistic robot can dispense many drugs for one prescription at the same time. To guarantee the sustainability of drug dispensation, it is required that FMDS replenish drugs rapidly. The traditional order picking model (OPM) is difficult to meet the demand of prompt replenishment. To solve the problems of prolonged refilling route and inefficiency of drugs replenishment, a mixed refilling model based on multiple steps traveling salesman problem model (MTSPM) and vehicle routing problem model (VRPM) is proposed, and it is deployed in two circumstances of FMDS, including temporary replenishment mode (TRM) and concentrate replenishment mode (CRM). It not only meted the demand under different circumstances of drug replenishment but also shortened the refilling route significantly. First, the new pick sets were generated. Then, the orders of pick sets were optimized and the new paths were achieved. When the number of pickings is varied no more than 20, experiment results declared that the refilling route is the shortest by utilizing MTSPM when working under the TRM condition. Comparing MTSPM with OPM, the rate of refilling route length decreased up to 32.18%. Under the CRM condition, the refilling route is the shortest by utilizing VRPM. Comparing VRPM with OPM, the rate of refilling route length decreased up to 58.32%. Comparing VRPM with MTSPM, the rate of refilling route length has dropped more than 43.26%.

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“…In other words, the application of image recognition and deep learning in logistics and supply chain field becomes a trend. For example, image recognition can help to guide the path of logistics and transportation, and it can solve the problem that several automatic transportation vehicles make mistakes because of large path identification errors [3]. Another example is fruit and vegetable classification.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Xue

Liu

2020

Complex Intell. Syst.

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Image recognition supports several applications, for instance, facial recognition, image classification, and achieving accurate fruit and vegetable classification is very important in fresh supply chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN), which uses a convolution autoencoder to pre-train the images and uses an attention-based DenseNet to extract the features of image. In the first part of the framework, an unsupervised method with a set of images is applied to pre-train the greedy layer-wised CAE. We use CAE structure to initialize a set of weights and bias of ADN. In the second part of the framework, the supervised ADN with the ground truth is implemented. The final part of the framework makes a prediction of the category of fruits. We use two fruit datasets to test the effectiveness of the model, experimental results show the effectiveness of the framework, and the framework can improve the efficiency of fruit sorting, which can reduce costs of fresh supply chain, factories, supermarkets, etc.

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RETRACTED ARTICLE: Research on path guidance of logistics transport vehicle based on image recognition and image processing in port area

Cited by 5 publications

References 16 publications

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Optimization for medical logistics robot based on model of traveling salesman problems and vehicle routing problems

A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

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