An automatic plant leaf stoma detection method based on YOLOv5

Li, Xin; Guo, Siyu; Gong, Linrui; Liu, Yuan

doi:10.1049/ipr2.12617

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…It is lighter, faster, and weights file is 90% smaller than YOLOv4, so it has higher accuracy and better small target recognition. YOLOv5 contains five versions, including YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, of which YOLOv5n has the smallest model [23]. This study will use the YOLOv5s model for smoke detection.…”

Section: Improved Yolov5s Modelmentioning

confidence: 99%

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

Chen

Xue

Zhu

et al. 2023

IET Image Processing

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There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops.

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Section: Improved Yolov5s Modelmentioning

confidence: 99%

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

Chen

Xue

Zhu

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Traditional target detection methods rely on color, texture, and shape features. Their processing speed is fast and less dependent on hardware, but they need to manually select features and have low robustness when disturbed by noise, which makes it difficult to meet the detection needs in complex environments [ 7 , 8 , 9 ]. In contrast, deep learning algorithms, despite having higher hardware requirements, automatically learn features from raw data, providing accurate detection in complex environments [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sun-Dried Kelp Detection: Introducing K-YOLO, a Lightweight Model with Improved Precision and Recall

Xiao,

Zhu,

Hong

et al. 2024

Sensors

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Kelp, often referred to as a “sea vegetable”, holds substantial economic significance. Currently, the drying process for kelp in China primarily relies on outdoor sun-drying methods. Detecting kelp in the field presents challenges arising from issues such as overlapping and obstruction. To address these challenges, this study introduces a lightweight model, K-YOLOv5, specifically designed for the precise detection of sun-dried kelp. YOLOv5-n serves as the base model, with several enhancements implemented in this study: the addition of a detection head incorporating an upsampling layer and a convolution module to improve the recognition of small objects; the integration of an enhanced I-CBAM attention mechanism, focusing on key features to enhance the detection accuracy; the replacement of the CBS module in the neck network with GSConv to reduce the computational burden and accelerate the inference speed; and the optimization of the IoU algorithm to improve the identification of overlapping kelp. Utilizing drone-captured images of sun-dried kelp, a dataset comprising 2190 images is curated. Validation on this self-constructed dataset indicates that the improved K-YOLOv5 model significantly enhances the detection accuracy, achieving 88% precision and 78.4% recall. These values represent 6.8% and 8.6% improvements over the original model, respectively, meeting the requirements for the real-time recognition of sun-dried kelp.

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“…There are currently many cases of machine learning in medicine such as Fang et al combined DCNN with multimodality images for AD classification [7]; Juan et al proposed a vestibule segmentation network for CT images under the basic encoder-decoder framework [8]. Machine learning is also applied in agriculture [9,10]. The method first requires data analysis of the features that play a critical factor in the prediction results, followed immediately by dimensionality reduction of the data, which aims to remove some features that have a low impact on classification while enhancing those that have a high impact on classification.…”

Section: Introductionmentioning

confidence: 99%

A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

An,

Sun,

Song

et al. 2023

IET Image Processing

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Traditional warehousing typically needs machine learning or manual tagging to classify objects. However, this method is less robust and consumes a lot of labour and material resources. Based on DenseNet, this work proposes a feature weighting convolutional network recognition model and designs a set of software and hardware for data acquisition, which is applied to the efficient classification of industrial parts in warehouse management. Firstly, this work modifies DenseNet by embedding SE‐Block, and replaces the cross‐entropy loss function with the focus loss function to optimize the model structure. Secondly, a multi‐view hardware and software acquisition system is designed to complete the functions of part image acquisition, image preprocessing, model training and part recognition. Finally, an industrial parts sorting experiment was designed. Compared with the original DenseNet model, the proposed weighted convolutional network identification model showed that the accuracy of the modified model was increased by 3.09% and the convergence rate was significantly improved. The modified model proposed in this work aims to improve the recognition accuracy of industrial parts in modern warehouse management, so as to modify the classification efficiency of warehouse parts in production.

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An automatic plant leaf stoma detection method based on YOLOv5

Cited by 10 publications

References 35 publications

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

Enhancing Sun-Dried Kelp Detection: Introducing K-YOLO, a Lightweight Model with Improved Precision and Recall

A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

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