Identifying Capsule Defect Based on an Improved Convolutional Neural Network

Zhou, Junlin; He, Jiao; Li, Guoli; Liu, Yongbin

doi:10.1155/2020/8887723

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…At present, deep learning-based detection approaches have two main types: image classification and object detection. Accordingly, Zhou et al [29] proposed a CNN-based defect detection model for pharmaceutical capsules. This model can classify various capsule defects, such as dent, hole, and stain.…”

Section: Deepmentioning

confidence: 99%

Surface Quality Automatic Inspection for Pharmaceutical Capsules Using Deep Learning

et al. 2022

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Capsules are commonly used as containers for most pharmaceutical products. Thus, the quality of a capsule is closely related to the therapeutic effect of the products and patient health. At present, surface quality testing is an essential task in the actual production of pharmaceutical capsules. In this study, a deep learning-based capsule defect detection model, called CapsuleDet, is proposed to classify and localize defects in image sensor data from capsule production for practical application. A guided filter-based image enhancement method and hybrid data augmentation method are used in improving the quality and quantity of the raw data, respectively, to mitigate the low contrast issue and enhance the robustness of the model training. Deformable convolution module and attentional fusion feature pyramid are also used to improve the detection effect of capsule defects by effectively utilizing the semantic and geometric information in the extracted feature maps and catering to the detection of defects with different shapes and scales. The evaluation results on the capsule defect dataset demonstrate that the proposed method achieves 92.91% mean average precision and 22.16 frames per second. Moreover, its overall performance in terms of training time, model size, detection accuracy, and speed is better than that of the currently popular detectors.

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Section: Deepmentioning

confidence: 99%

Surface Quality Automatic Inspection for Pharmaceutical Capsules Using Deep Learning

et al. 2022

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“…With the advancement of deep learning techniques, neural networks have become a prominent tool for capsule surface defect detection [3]. Junlin Zhou et al [4] proposed an improved Convolutional Neural Network (CNN) method called RACNN, which achieved high accuracy on a capsule dataset but exhibited low accuracy in recognizing deformed capsules. Zhiyuan Wang et al [5] proposed an SVMbased complex component detection method that outperformed traditional CNN models in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Capsule Defect Detection Method Based on an Improved Model

Zhao,

Dai,

Chen

et al. 2024

Advances in Transdisciplinary Engineering

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This article presents a novel method based on an enhanced version of the YOLOv5 model for detecting surface defects on capsules. The paper addresses the challenge of detecting defects on transparent capsules by introducing a deep learning-based approach called M-YOLO. Firstly, the backbone layer is replaced with MobileNetV3, enhancing the model’s suitability for scenarios with limited storage space and power consumption. Secondly, a Cross-channel-H-SPP (CH-SPP) module is devised to augment the contextual information within the sensory field. To enhance defect detection accuracy, the SE attention mechanism is incorporated. Additionally, an improved label assignment strategy is employed to enhance the recall rate. Experimental results on the dataset demonstrate significant improvements in both accuracy and speed compared to the YOLOv5 model. The algorithm proposed in this article satisfies the requirement of processing every second (specific data).

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“…It is very sensitive to hyperparameter variations and has only three convolutional layers, leaving much to be desired [19]. The method of deep edge guidance feedback residual network, which decompositions image signals into different frequency bands, reconstructs them and then combines them [20]. In this way, important details of images can be retained, which preliminarily solves the problem that CNN has not fully developed prior information of images and has lost details.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction

Wang

2023

International Journal of Digital Multimedia Broadcasting

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With the development of film and television industry and the rise of new media short video, film and television postproduction process has higher and higher requirements for image quality. In film and television postproduction, optimizing the image quality can enhance the resolution and make the image more vivid and detailed. High-quality image can fully embody the value of film and television, as well as promote the development of new media short videos. This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. This technology can enhance the ability to extract image features, improve the accuracy of image feature extraction, and then improve the image resolution and details, thus improving the image quality in the process of film and television postproduction. The results show that the average value of PSNR is 30.29. The average value of PSNR of the proposed algorithm is higher than that of other algorithms, indicating that the error between the image processed by the proposed algorithm and the original image is small. The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. Compared with other algorithms, the structure processed by the algorithm in this paper is more similar to the original structure, resulting in a better graph. The algorithm in this paper has the best performance on both the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM). The improved image processing technology proposed in this paper can effectively improve the accuracy of image feature extraction, making film and television or new media short video images of higher quality.

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Identifying Capsule Defect Based on an Improved Convolutional Neural Network

Cited by 10 publications

References 22 publications

Surface Quality Automatic Inspection for Pharmaceutical Capsules Using Deep Learning

Surface Quality Automatic Inspection for Pharmaceutical Capsules Using Deep Learning

Capsule Defect Detection Method Based on an Improved Model

Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction

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