Development of an optimally designed real-time automatic citrus fruit grading–sorting​ machine leveraging computer vision-based adaptive deep learning model

Chakraborty, Subhasish; Subeesh, A.; Dubey, Kumkum; Jat, Dilip; Chandel, Narendra Singh; Potdar, R.R.; Rao, N. V. Subba; Kumar, Deepak

doi:10.1016/j.engappai.2023.105826

Cited by 27 publications

(6 citation statements)

References 35 publications

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“…Generally, current mainstream computer vision algorithm model evaluation indicators include accuracy and performance [38]. The index used to measure the accuracy of the target detection algorithm is generally the AP, and the performance index includes the FLOPs, FPS, and video memory occupations.…”

Section: Evaluation Indexmentioning

confidence: 99%

Crack Detection of Concrete Based on Improved CenterNet Model

Kang,

Zhou,

Gao

et al. 2024

Applied Sciences

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Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.

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Section: Evaluation Indexmentioning

confidence: 99%

Crack Detection of Concrete Based on Improved CenterNet Model

Kang,

Zhou,

Gao

et al. 2024

Applied Sciences

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“…Therefore, there is an urgent need to develop an automatic, non-destructive cucumber grading technology and corresponding equipment. With the widespread application of machine vision and mechatronics technology in agricultural production [6][7][8][9], an effective approach is provided for the design of a cucumber grading machine.…”

Section: Introductionmentioning

confidence: 99%

Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader

Liu,

Zhang,

et al. 2024

Foods

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The North China type cucumber, characterized by its dense spines and top flowers, is susceptible to damage during the grading process, affecting its market value. Moreover, traditional manual grading methods are time-consuming and labor-intensive. To address these issues, this paper proposes a cucumber quality grader based on machine vision and deep learning. In the electromechanical aspect, a novel fixed tray type grading mechanism is designed to prevent damage to the vulnerable North China type cucumbers during the grading process. In the vision grading algorithm, a new convolutional neural network is introduced named MassNet, capable of predicting cucumber mass using only a top-view image. After obtaining the cucumber mass prediction, mass grading is achieved. Experimental validation includes assessing the electromechanical performance of the grader, comparing MassNet with different models in predicting cucumber mass, and evaluating the online grading performance of the integrated algorithm. Experimental results indicate that the designed cucumber quality grader achieves a maximum capacity of 2.3 t/hr. In comparison with AlexNet, MobileNet, and ResNet, MassNet demonstrates superior cucumber mass prediction, with a MAPE of 3.9% and RMSE of 6.7 g. In online mass grading experiments, the grading efficiency of the cucumber quality grader reaches 93%.

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“…Servadei, L. et al used machine learning principles to design a cost estimation strategy suitable for the field of computer vision, which has better accuracy than traditional methods and greatly reduces labor costs [13]. Chakraborty, S. K. et al conceived a fruit grading machine with a visual sorting function on the basis of modern intelligent sensing equipment, and the newly designed fruit grading machine showed good fruit sorting performance in practical feedback [14]. Rusli, L. et al identified and tested the performance of machine vision cameras to identify and distinguish fasteners and found that the accuracy of fastener recognition can only be ensured by adjusting the camera device to appropriate threshold parameters and enabling multiple frontal frame recognition functions [15].…”

Section: Introductionmentioning

confidence: 99%

Research on the application of machine vision in the design of machine automation

Zhang,

2024

Applied Mathematics and Nonlinear Sciences

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The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.

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Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model

Cited by 27 publications

References 35 publications

Crack Detection of Concrete Based on Improved CenterNet Model

Crack Detection of Concrete Based on Improved CenterNet Model

Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader

Research on the application of machine vision in the design of machine automation

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Development of an optimally designed real-time automatic citrus fruit grading–sorting​ machine leveraging computer vision-based adaptive deep learning model

Cited by 27 publications

References 35 publications

Crack Detection of Concrete Based on Improved CenterNet Model

Crack Detection of Concrete Based on Improved CenterNet Model

Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader

Research on the application of machine vision in the design of machine automation

Contact Info

Product

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Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model