Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification

Tan, Lu; Huangfu, Tianran; Wu, Liyao; Chen, Wenying

doi:10.1186/s12911-021-01691-8

Cited by 119 publications

(46 citation statements)

References 22 publications

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“…Additionally, YOLOv4-tiny has a higher FPS, which leads to faster performance [24,33]. Due to the fact that YOLOv4-tiny is a modified version of YOLOv3, its accuracy increased [34], and YOLOv3 already outperformed SSD and faster R-CNN [35]. A mobile client-server application for price tag data verification was also developed based on the study's results.…”

Section: Discussionmentioning

confidence: 99%

Neural Network-Based Price Tag Data Analysis

et al. 2022

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This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an individual dataset collected by the authors. Additionally, this paper covers the automatic image text recognition approach using EasyOCR API. Research revealed that the optimal network for segmentation is YOLOv4-tiny, featuring a cross validation accuracy of 96.92%. EasyOCR accuracy was also calculated and is 95.22%.

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Section: Discussionmentioning

confidence: 99%

Neural Network-Based Price Tag Data Analysis

et al. 2022

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“…For example, YOLO is considered among the fastest object recognition methods. However, R-CNN is often more accurate; for technical comparison between these methods, the reader is referred to [40] . But overall, this progress in the development of object recognition methods has also helped solve some complex CV tasks such as detecting and tracking objects of interest in video footage [41][42][43] , which is an important and very common task in robotic surgery.…”

Section: Figurementioning

confidence: 99%

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Elyan¹,

Vuttipittayamongkol²,

Johnston³

et al. 2022

Art Int Surg

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The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions.

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“…SSD has improved versions such as Deconvolutional SSD (DSSD) that includes large-scale context in object detection, Rainbow SSD (RSSD) that concatenates different feature maps using deconvolution and batch normalisation [ 73 ], and Feature-fusion SSD (FSSD) that balances semantic and positional information using bilinear interpolation to resize feature maps to the same size to be subsequently concatenated [ 74 ]. The comparison of different architectures for real-time applications presented in [ 75 ] also mentions RetinaNet because it has higher accuracy, but it is not recommended for real-time applications, as it has a frame rate lower than 25 frames per second (FPS). EdgeEye [ 76 ] proposes an edge computing framework to analyse real-time video with a mean of 55 FPS as inference speed.…”

Section: Vision Modulementioning

confidence: 99%

Vision-Based Module for Herding with a Sheepdog Robot

Castillo

Sánchez-González

Campazas-Vega

et al. 2022

Sensors

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Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.

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Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification

Cited by 119 publications

References 22 publications

Neural Network-Based Price Tag Data Analysis

Neural Network-Based Price Tag Data Analysis

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Vision-Based Module for Herding with a Sheepdog Robot

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