Non-Destructive Internal Defect Detection of In-Shell Walnuts by X-ray Technology Based on Improved Faster R-CNN

Zhang, Hui; Ji, Shuai; Shao, Mingming; Pu, Houxu; Zhang, Liping

doi:10.3390/app13127311

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The mainstream method for two-stage detection algorithms is the Region-CNN series [3][4][5]. For example, Zhang et al [6] presented an improved faster R-CNN model for detecting in-shell walnuts with shriveled and empty-shell defects. They incorporated a feature pyramid network and utilized the region-of-interest alignment and softer NMS modules to enhance the model's detection precision.…”

Section: Related Work 21 Defect Detectionmentioning

confidence: 99%

MCAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects

Yu,

et al. 2023

Applied Sciences

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Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this approach, we could enhance the production workflow, boost production efficiency, minimize company expenses, and lessen the workload on workers. In this paper, we propose a lightweight tile-defect detection algorithm that strikes a balance between model parameters and accuracy. Firstly, we introduced the mobile-friendly vision transformer into the backbone network to capture global and local information. This allowed the model to comprehend the image content better and enhance defect feature extraction. Secondly, we designed a lightweight feature fusion network. This design amplified the network’s detection capability for defects of different scales and mitigated the blurriness and redundancy in the feature maps while reducing the model’s parameter count. We then devised a convolution module incorporating the normalization-based attention module, to direct the model’s focus toward defect features. This reduced background noise and filtered out features irrelevant to defects. Finally, we utilized a bounding box regression loss with a dynamic focusing mechanism. This approach facilitated the prediction of more precise object bounding boxes, thereby improving the model’s convergence rate and detection precision. Experimental results demonstrated that the improved algorithm achieved a mean average precision of 71.9%, marking a 3.1% improvement over the original algorithm. Furthermore, there was a reduction of 26.2% in the model’s parameters and a 20.9% decrease in the number of calculations.

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Section: Related Work 21 Defect Detectionmentioning

confidence: 99%

MCAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects

Yu,

et al. 2023

Applied Sciences

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Section: Data Acquisition and Processing Of Walnut Shells And Kernelsmentioning

confidence: 99%

“…The pictures were taken at a resolution of 3904 pixels × 2928 pixels in March 2023. The quality of the photos was primarily evaluated based on traditional personal perception [23,24], which generally met the practical criteria for shell and kernel separation conditions. During walnut shell-kernel detection, numerous factors, such as walnut species, illumination, and shell-kernel distribution, may generate differences in walnut shell-kernel pictures and thereby influence their recognition effect (Figure 2).…”

Section: Data Acquisition and Processing Of Walnut Shells And Kernelsmentioning

confidence: 99%

Machine Vision-Based Chinese Walnut Shell–Kernel Recognition and Separation

Zhang,

Wang,

Liu

et al. 2023

Applied Sciences

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Walnut shell–kernel separation is an essential step in the deep processing of walnut. It is a crucial factor that prevents the increase in the added value and industrial development of walnuts. This study proposes a walnut shell–kernel detection method based on YOLOX deep learning using machine vision and deep-learning technology to address common issues, such as incomplete shell–kernel separation in the current airflow screening, high costs and the low efficiency of manually assisted screening. A dataset was produced using Labelme by acquiring walnut shell and kernel images following shellshock. This dataset was transformed into the COCO dataset format. Next, 110 epochs of training were performed on the network. When the intersection over the union threshold was 0.5, the average precision (AP), the average recall rate (AR), the model size, and floating point operations per second were 96.3%, 84.7%, 99 MB, and 351.9, respectively. Compared with YOLOv3, Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot MultiBox Detector algorithms (SSD), the AP value of the proposed algorithm was increased by 2.1%, 1.3%, and 3.4%, respectively. Similarly, the AR was increased by 10%, 2.3%, and 9%, respectively. Meanwhile, walnut shell–kernel detection was performed under different situations, such as distinct species, supplementary lighting, or shielding conditions. This model exhibits high recognition and positioning precision under different walnut species, supplementary lighting, and shielding conditions. It has high robustness. Moreover, the small size of this model is beneficial for migration applications. This study’s results can provide some technological references to develop faster walnut shell–kernel separation methods.

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