Automatic Drug Pills Detection based on Convolution Neural Network

Ou, Yang-Yen; Tsai, An-Chao; Wang, Jhing-Fa; Lin, Jim‐Min

doi:10.1109/icot.2018.8705849

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…In the research by Ou et al, models based on convolutional neural networks were used to detect and classify medical pills in images. In 2018 [25], an improved model of Inceptionv3 [26] was used, wherein models were trained using a newly collected dataset. The prepared dataset consisted of more than 470,000 images, where each category (different types of medical pills, for a total of 131 categories) had approximately 3600 images, taken from various angles.…”

Section: Related Workmentioning

confidence: 99%

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

Kvietkauskas,

Pavlov,

Stefanovič

et al. 2024

Applied Sciences

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Computer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image data, different problems arise in relation to the building of models suitable for various solutions. This paper investigates the influence of parameters used in the training process involved in detecting similar kinds of objects, i.e., the hyperparameters of the algorithm and the training parameters. This experimental investigation focuses on the widely used YOLOv5 algorithm and analyses the performance of different models of YOLOv5 (n, s, m, l, x). In the research, the newly collected construction details (22 categories) dataset is used. Experiments are performed using pre-trained models of the YOLOv5. A total of 185 YOLOv5 models are trained and evaluated. All models are tested on 3300 images photographed on three different backgrounds: mixed, neutral, and white. Additionally, the best-obtained models are evaluated using 150 new images, each of which has several dozen construction details and is photographed against different backgrounds. The deep analysis of different YOLOv5 models and the hyperparameters shows the influence of various parameters when analysing the object detection of similar objects. The best model was obtained when the YOLOv5l was used and the parameters are as follows: coloured images, image size—320; batch size—32; epoch number—300; layers freeze option—10; data augmentation—on; learning rate—0.001; momentum—0.95; and weight decay—0.0007. These results may be useful for various tasks in which small and similar objects are analysed.

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Section: Related Workmentioning

confidence: 99%

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

Kvietkauskas,

Pavlov,

Stefanovič

et al. 2024

Applied Sciences

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“…However, most of the current detection methods are invasive and destructive detection methods, such as chemical detection methods and chromatograph detection methods. Not only are the detection speeds slow to achieve mass detection, but they also destroy the appearance of drugs and affect the quality of the products which is harmful to the second sale [92,[109][110][111]. For the detection of counterfeit drugs, Shinde et al, [109] combined visible and near-infrared hyperspectral imaging equipment and proposed a multi-layer perceptron method.…”

Section: Detection Of Counterfeit Drugs With Hyperspectral Imagingmentioning

confidence: 99%

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Wang

Mao

et al. 2022

J Intell Robot Syst

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The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

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“…The training set included 131 categories and a total of 1,680 images for training. The top-1 accuracy rate for the trained network was up to 79.4% [18]. Based on these studies, deep learning has gradually replaced manual design extraction in pill feature extraction, and deep learning algorithms, such as LeNet, AlexNet, and ResNet, are able to address the problem of pill image classification.…”

Section: Introductionmentioning

confidence: 99%

Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification

Tan

Huangfu

et al. 2021

Preprint

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Background: The correct identification of pills is very important to ensure the safe administration of drugs to patients. We used three currently mainstream object detection models, respectively Faster R-CNN, Single Shot Multi-Box Detector (SSD), and You Only Look Once v3(YOLO v3), to identify pills and compare the associated performance.Methods: In this paper, we introduce the basic principles of three object detection models. We trained each algorithm on a pill image dataset and analyzed the performance of the three models to determine the best pill recognition model. Finally, these models are then used to detect difficult samples and compare the results.Results: The mean average precision (MAP) of Faster R-CNN reached 87.69% but YOLO v3 had a significant advantage in detection speed where the frames per second (FPS) was more than eight times than that of Faster R-CNN. This means that YOLO v3 can operate in real time with a high MAP of 80.17%. The YOLO v3 algorithm also performed better in the comparison of difficult sample detection results. In contrast, SSD did not achieve the highest score in terms of MAP or FPS.Conclusion: Our study shows that YOLO v3 has advantages in detection speed while maintaining certain MAP and thus can be applied for real-time pill identification in a hospital pharmacy environment.

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Automatic Drug Pills Detection based on Convolution Neural Network

Cited by 14 publications

References 9 publications

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification

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