Study on the Detection of Defoliation Effect of an Improved YOLOv5x Cotton

Wang, Xingwang; Wang, Xufeng; Hu, Can; Dai, Fei; Xing, Jianfei; Wang, Enyuan; Du, Zhenhao; Wang, Long; Guo, Weihua

doi:10.3390/agriculture12101583

Cited by 5 publications

(2 citation statements)

References 12 publications

(15 reference statements)

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“…On top of this, by adding SPSs, the final model achieved an mAP of 89.98 on the base images. Secondly, as shown in Table 3, we compared our proposed RDT-FSDet model with the LightR-YOLOv5 model proposed by Wang et al 18 and YOLOv5x 19 , and our model achieved better accuracy. Recall measured the ability of the model to successfully find positive samples.…”

Section: Methodsmentioning

confidence: 94%

RDT-FSDet: Few-shot Object Detection for Rapid Antigen Test

Duan,

Wang,

Tan

et al. 2023

Interdisciplinary Nursing Research

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Objective: Manual verification of rapid diagnostic test (RDT) results is a time-consuming task; therefore, it is essential to introduce an object detection model into RDT result recognition to reduce the time involved. To address these problems, a detector that can rapidly adapt to different RDT results in various regions is important. Methods: We employed the few-shot object detection strategy and trained the Faster R-CNN detector with the mainland data set as the base class, followed by fine-tuning with the few-shot approach on the Macau RDT result data set. Moreover, we introduced 2 novel data augmentation methods, namely the “light simulation mask method” and “synthetic positive samples” for an unbalanced data set, to increase the sample size and balance the data set of the RDT detection task. Result: Compared with LightR-YOLOv5, RDT-few-shot detector (FSDet) achieved an mean average precision of 91.18 and a recall of 93.59 on the Macau RDT data set, demonstrating that this model can rapidly adapt to RDT results in different regions. The inference time of RDT-FSDet for each RDT result was 0.14 seconds, which can save ~90% of the detection time compared with manual screening. Conclusion: In addition to its application in the context of the coronavirus disease 2019 pandemic, this model can also be used as a general small-sample detection model. RDT-FSDet can be applied to the detection tasks of other small data sets, such as managing and analyzing detection results in other or future epidemics.

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Section: Methodsmentioning

confidence: 94%

RDT-FSDet: Few-shot Object Detection for Rapid Antigen Test

Duan,

Wang,

Tan

et al. 2023

Interdisciplinary Nursing Research

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“…Although these models share similar structures, they have distinguishing features such as different numbers of convolutional layers. Increasing convolutional layers allows for a thicker feature map and strengthens the network’s ability to learn to extract features [ 19 ]. YOLOv5 has a rapid detection model, with a runtime of only 0.07 s per frame [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study

Ozsunkar,

Özen,

Abdelkarim

et al. 2024

BMC Oral Health

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Background Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. Methods A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. Results Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model’s performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. Conclusions The model’s accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. Clinical revelance Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

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