Traditional Otsu Algorithm Research Articles

Tool Damage Detection Method Based on Improved Threshold Segmentation Algorithm

In order to solve the problem that the current tool wear defects are difficult to be collected by the visual inspection system, an Otsu threshold segmentation algorithm based on particle swarm optimization is proposed.to detect tool wear The algorithm improved update strategy for inertia coefficients which effectively expanding the search scope of the algorithm and shortens the running time of the algorithm. By adding a perturbation equation to the particle swarm, solved the problem of traditional particle swarm optimization algorithms easily falling into local optima. Finally, an experimental platform is built to verify the effectiveness of the algorithm. This inspection method can achieve the identification of tool damage areas and the measurement of tool damage amount, and has advantages such as high recognition accuracy and fast running speed compared to traditional Otsu algorithm, Canny algorithm ,local threshold segmentation and so on. The research results have certain reference value for the actual tool defect detection system.

Research on Sheepskin Contour Extraction Method Based on Computer Vision Measurement Technology

Manual trimming of sheepskin is intensive labor, and the working environment is full of rotten smells. The tannery is facing increasingly severe recruitment difficulties. This paper uses computer vision technology to study automatic recognition of sheepskin contours, which is the basis for the subsequent automatic trimming of sheepskin. After observing and analyzing the raw sheepskin images collected by an industrial array camera, a method of sheepskin contour extraction based on computer vision measurement technology is proposed in this paper. This method uses the fast Otsu threshold algorithm based on the pixel set to perform binary image segmentation. Combined with morphological processing for edge defect filling and topology analysis of boundary contour tracking algorithm to extract maximum contour information, it has a pixel-level three-dimensional de-noising preprocessing function and can accurately extract the sheepskin contour in the raw sheepskin image. The experimental results show that using the fast Otsu threshold algorithm proposed in this paper for binary segmentation to extract sheepskin contours, the detection rate is nearly 160% faster than the traditional Otsu algorithm, the edge protection is better, the error segmentation is reduced by nearly 3% and it has good anti-noise performance. It can meet the industrial production requirements of subsequent automatic cutting of sheepskin.

Image Segmentation of Pitaya Disease Based on Genetic Algorithm and Otsu Algorithm

Aiming at the problem of using traditional Otsu algorithm to segment pitaya disease images, this paper proposes the Otsu algorithm combined with genetic algorithm. The maximum variance between classes obtained by Otsu is calculated as the fitness value of the genetic algorithm to improve accuracy and improve efficiency of optimization. The experimental results show that the segmentation accuracy and efficiency of the algorithm in this paper are higher, and it has certain practical value.

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

AN OTSU image segmentation based on fruitfly optimization algorithm

Despite its simplicity and high accuracy, the real-time efficiency of OTSU segmentation is not high. In order to promote the real-timeliness of image segmentation, this paper introduces the fruitfly optimization algorithm (FOA) to OTSU segmentation, creating an FOA-OTSU segmentation algorithm. In the proposed algorithm, the optimal threshold for segmentation is searched for by the FOA. The classic Lena picture, Flower picture and Cameraman picture were used for simulation experiment, and the results were evaluated by signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR). The results show that the segmentation time is reduced by about 50.0% on the premise of the PSNR and SNR evaluation criteria and the segmentation effect basically unchanged. The simulations indicate that our method converges faster and consumes fewer time than traditional OTSU algorithm, without sacrificing the segmentation accuracy. The research provides a desirable tool with high real-time performance of rapid image segmentation.

An Otsu multi-thresholds segmentation algorithm based on improved ACO

For the traditional multi-thresholds segmentation algorithms, usually it would take too much time in finding the optimal solution. As one of the widely used swarm-intelligence optimization algorithms, ant colony optimization (ACO) algorithm has been introduced to optimize the thresholding search process. The traditional ACO is improved in this paper to get a faster convergence speed and applied in Otsu multi-thresholds segmentation algorithms. When the ant colony is initialized, each member of the ant colony is distributed evenly in the solution space, so that it could search the entire solution space as fast as possible. In the search process, the random step length of ants moving is generated by the Levy flight pattern, but the global transition probability of the traditional ACO is used to control the search range of the ant colony. The experimental results show that the proposed algorithm could obtain the optimal thresholds faster and more effectively than the traditional Otsu algorithm and the Otsu based on traditional ACO.

Research on Pest Image Processing Method Based on Android Thermal Infrared Lens

Agricultural pests and diseases seriously affect the yield and quality of crops. In order to study the incidence of pests and diseases, and improve the utilization rate of pesticides, an Android rapid processing system of Android-based insect pest thermal infrared image was designed and implemented. In this paper, maize tumor powdery mildew was used as the research object, infrared thermal imaging lens based on Android system was used to obtain corn disease images. Meanwhile, OpenCV technology was used for image processing. Moreover, in order to better achieve the thermal infrared segmentation effect, the image segmentation effect based on the traditional Otsu algorithm (Otsu) and the Otsu single-threshold segmentation method based on the bare bones fireworks algorithm (BBFWA) was analyzed. Importantly, the results showed that the method of Otsu single-threshold segmentation based on BBFWA had the best effect on thermal infrared image segmentation. Since thermal infrared images are greatly influenced by the ambient temperature, the shooting needs to be performed under cloudy or cloudy conditions. Finally, the ratio value between the total number of pixels in the white area and the total number of pixels in the divided binary image was calculated to determine the degree of pest infestation, which can provide data reference for the UAV spraying amount to some extent.

Normalised Otsu's Segmentation Algorithm for Melanoma Diagnosis

Melanoma is a deadly skin cancer which increases the death rate at a faster rate. In order to bring the death rate under control, melanoma should be detected at its earlier stage. To achieve this, researchers have introduced Computer aided diagnosis and adopted the same. In this technique, is found to be one of the important steps. Many algorithms exist in practise for segmentation,where one of the important algorithms is Traditional Otsu Algorithm. In this algorithm the major drawback is that the segmentation is improper in the presence of variable illumination. This paper proposes an algorithm Normalised Otsu Segmentation which overcomes the above mentioned drawback and results in an accurate segmentation. This algorithm first normalises the image to overcome variable illumination and then segments the image using Otsu algorithm. The accurate result given by this algorithm can be used in further steps to detect the lesion accurately which will provide a hand a for reducing the death rate.

Study of improved Otsu algorithm and its ration evaluation analysis for PCB photoelectric image segmentation

The algorithm of maximum variance between clusters (traditional Otsu algorithm) is discussed, and its advantage is given also. In order to segment the PCB photoelectric image better, on the basis of the traditional Otsu algorithm, considering the different influence of image segmentation about the factors of the distance between target and background as well as each kind of cohesion, an improved Otsu algorithm is proposed, and its basic principle and segmentation advantages are analyzed in detail. In order to evaluate these segmentation results impersonally by using different algorithms, the quantitative criteria of gray-level contrast and district interior uniformity are adopted to evaluate these segmentation results impersonally. Finally, the different segmentation experiment contrasts of PCB photoelectric image between our algorithm and other algorithms is executed, the results of experiment indicate that our algorithm has relatively better segmentation quality.

Rayleigh-distribution based minimum error thresholding for SAR images

This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.