Adaptive fractional-order genetic-particle swarm optimization Otsu algorithm for image segmentation

Chen, Liping; Gao, Jinhui; Lopes, António M.; Zhang, Zhiqiang; Chu, Zhaobi; Wu, Ranchao

doi:10.1007/s10489-023-04969-8

Cited by 13 publications

(5 citation statements)

References 50 publications

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“…The specific details of the experimental running environment can be found in Table 3. The cucumber sample image undergoes several preprocessing stages, such as converting it to grayscale, using histogram equalization to enhance the image and improve its edge information, and binarizing it using the global thresholding OTSU method [32][33][34]. This approach iterates through all the different grayscale values in order to identify the ideal threshold, denoted as T = 126, that maximizes the interclass variance (ICV).…”

Section: Laboratory Settingmentioning

confidence: 99%

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Liu,

Sun,

Xia

et al. 2024

Applied Sciences

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Accurate identification of fruits in greenhouse environments is an essential need for the precise functioning of agricultural robots. This study presents a solution to the problem of distinguishing cucumber fruits from their stems and leaves, which often have similar colors in their natural environment. The proposed algorithm for cucumber fruit identification relies on color segmentation and form matching. First, we get the boundary details from the acquired image of the cucumber sample. The edge information is described and reconstructed by utilizing a shape descriptor known as the Fourier descriptor in order to acquire a matching template image. Subsequently, we generate a multi-scale template by amalgamating computational and real-world data. The target image is subjected to color conditioning in order to enhance the segmenacktation of the target region inside the HSV color space. Then, the segmented target region is compared to the multi-scale template based on its shape. The method of color segmentation decreases the presence of unwanted information in the target image, hence improving the effectiveness of shape matching. An analysis was performed on a set of 200 cucumber photos that were obtained from the field. The findings indicate that the method presented in this study surpasses conventional recognition algorithms in terms of accuracy and efficiency, with a recognition rate of up to 86%. Moreover, the system has exceptional proficiency in identifying cucumber targets within greenhouses. This attribute renders it a great resource for offering technical assistance to agricultural robots that operate with accuracy.

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Section: Laboratory Settingmentioning

confidence: 99%

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Liu,

Sun,

Xia

et al. 2024

Applied Sciences

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“…) Otsu's Algorithm Processes Regional Binarized Images: Otsu's algorithm, also known as Otsu's thresholding method, automatically determines the threshold by maximizing the inter-class variance, effectively separating the image foreground from the background. It is widely used in image processing and visual analysis [38,39]. In the experiments, Otsu's method is utilized to differentiate between burning and nonburning areas, enhancing the role of high-temperature pixels such as flames in image segmentation.…”

Section: Dynamic Brightness Temperature Threshold Inversionmentioning

confidence: 99%

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Ding,

Wang,

et al. 2024

Forests

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Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.

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“…Yousri et al [9] employed fractional order calculus and a cuckoo search algorithm to identify the parameters of fractional order-chaotic, noisy-chaotic, and hyper-chaotic financial systems and it operated with a greater efficiency. However, there are a few researchers that have combined fractional order and population intelligence algorithms for image segmentation [10].…”

Section: Introductionmentioning

confidence: 99%

Otsu Image Segmentation Based on a Fractional Order Moth–Flame Optimization Algorithm

Fan,

Ma,

Wang

et al. 2024

Fractal Fract

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To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image segmentation algorithm. Utilizing the advantages of memorability and heritability in fractional order differentiation, the position updating of moths is controlled by fractional order. Using the adaptive fractional order, the positions of moths are used to adjust the fractional order adaptively to improve the convergence speed. Combining the improved MFO algorithm with the two-dimensional Otsu algorithm, the optimization objective function is achieved by using its dispersion matrix. The experimental results indicate that, compared with traditional MFO, the convergence rate of the proposed algorithm is improved by about 74.62%. Furthermore, it has better segmentation accuracy and a higher fitness value than traditional MFO.

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Adaptive fractional-order genetic-particle swarm optimization Otsu algorithm for image segmentation

Cited by 13 publications

References 50 publications

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Otsu Image Segmentation Based on a Fractional Order Moth–Flame Optimization Algorithm

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