Target Positioning and Sorting Strategy of Fruit Sorting Robot Based on Image Processing

Yu, Juebang; Miao, Weijie; Zhang, Guangben; Li, Kai; Shi, Yinggang; Liu, Li

doi:10.18280/ts.380326

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…One of the additional improvement directions of ATC-YOLOv5 is network lightweightness. In the context of smart agriculture and automated fruit production, if ATC-YOLOv5 is used for passion fruit quality grading tasks, it is likely to be deployed on automated sorting machines or robots [66], which imposes certain requirements on the network's size, detection speed, and computational efficiency. Among the introduced improvements in this study, both iAFPN and TRCSP can increase the mAP while reducing the number of parameters and GFLOPs.…”

Section: Performance Comparison and Analysismentioning

confidence: 99%

ATC-YOLOv5: Fruit Appearance Quality Classification Algorithm Based on the Improved YOLOv5 Model for Passion Fruits

Liu,

Lin,

Feng

et al. 2023

Mathematics

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Passion fruit, renowned for its significant nutritional, medicinal, and economic value, is extensively cultivated in subtropical regions such as China, India, and Vietnam. In the production and processing industry, the quality grading of passion fruit plays a crucial role in the supply chain. However, the current process relies heavily on manual labor, resulting in inefficiency and high costs, which reflects the importance of expanding the application of fruit appearance quality classification mechanisms based on computer vision. Moreover, the existing passion fruit detection algorithms mainly focus on real-time detection and overlook the quality-classification aspect. This paper proposes the ATC-YOLOv5 model based on deep learning for passion fruit detection and quality classification. First, an improved Asymptotic Feature Pyramid Network (APFN) is utilized as the feature-extraction network, which is the network modified in this study by adding weighted feature concat pathways. This optimization enhances the feature flow between different levels and nodes, allowing for the adaptive and asymptotic fusion of richer feature information related to passion fruit quality. Secondly, the Transformer Cross Stage Partial (TRCSP) layer is constructed based on the introduction of the Multi-Head Self-Attention (MHSA) layer in the Cross Stage Partial (CSP) layer, enabling the network to achieve a better performance in modeling long-range dependencies. In addition, the Coordinate Attention (CA) mechanism is introduced to enhance the network’s learning capacity for both local and non-local information, as well as the fine-grained features of passion fruit. Moreover, to validate the performance of the proposed model, a self-made passion fruit dataset is constructed to classify passion fruit into four quality grades. The original YOLOv5 serves as the baseline model. According to the experimental results, the mean average precision (mAP) of ATC-YOLOv5 reaches 95.36%, and the mean detection time (mDT) is 3.2 ms, which improves the mAP by 4.83% and the detection speed by 11.1%, and the number of parameters is reduced by 10.54% compared to the baseline, maintaining the lightweight characteristics while improving the accuracy. These experimental results validate the high detection efficiency of the proposed model for fruit quality classification, contributing to the realization of intelligent agriculture and fruit industries.

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Section: Performance Comparison and Analysismentioning

confidence: 99%

ATC-YOLOv5: Fruit Appearance Quality Classification Algorithm Based on the Improved YOLOv5 Model for Passion Fruits

Liu,

Lin,

Feng

et al. 2023

Mathematics

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“…In particular, in the field of industrial automation production, the IoT boasts a good prospect. It has replaced the traditional manual statistics, changed the traditional supervision, and realized the safe and reliable supervision of modern production lines [10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Distributed Simulation of Industrial Automation Production Line Management

Wang¹,

Zhang²,

Zhang³

et al. 2022

Int. j. simul. model.

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The IoT maximizes the information and intelligence of large-scale producers, and greatly improves the supervision quality and efficiency of production lines. Existing research fails to use modern logistics simulation for distributed simulation, verification and analysis of industrial automated production line management (IAPLM). Thus, this paper studies the distributed simulation of IoT-based IAPLM. The time domain, component information and other elements are introduced into the distributed Petri network model to realize the optimization of the logic model of the industrial automation production line. The key points of distributed simulation design of IAPLM are introduced in detail, and the timing design of the production process, the mapping of logical model to distributed simulation platform, the structured processing of models, and the construction of simulation strategies for production equipment control are completed. Experimental results verify the effectiveness of the proposed model.

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Study on mango ripeness detection on production line based on improved YOLOv8s

Huang,

Jiang,

Zhou

et al. 2024

Food Measure

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Target Positioning and Sorting Strategy of Fruit Sorting Robot Based on Image Processing

Cited by 4 publications

References 25 publications

ATC-YOLOv5: Fruit Appearance Quality Classification Algorithm Based on the Improved YOLOv5 Model for Passion Fruits

ATC-YOLOv5: Fruit Appearance Quality Classification Algorithm Based on the Improved YOLOv5 Model for Passion Fruits

IoT-Based Distributed Simulation of Industrial Automation Production Line Management

Study on mango ripeness detection on production line based on improved YOLOv8s

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