# An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm
## 1 Introduction  
Image segmentation is an important part of image processing in practice [1; 2; 3; 4; 5], as well as a key image analysis technique [6; 7; 8; 9; 10]. The threshold image segmentation method divides pixels into several classes by setting the threshold value to realize the separation of the object and the background in the image. Threshold segmentation based on a histogram is the most widely used image segmentation method. The histogram is used for analysis. A better threshold is selected according to the relationship between the peaks and troughs of the histogram [11; 12; 13].  
Shannon, Otsu, and Kapur are the most common methods. Shannon entropy is used to determine the amount of information contained in a random data source [14]. The OTSU algorithm is an automaticmethod to determine the threshold using the maximum interclass variance [15]. Kapur entropy is the pixel grey value with the maximum entropy of the whole image [16]. Two-dimensional OTSU algorithm by [PERSON] and [PERSON] integrates the average grey value of neighborhood pixels on the basis of one-dimensional OTSU [17].  
Heuristic algorithm is proposed relative to the optimization algorithm, in which genetic algorithm (GA) [18], particle swarm optimizer (PSO) [19], and grey wolf optimizer algorithm (GWO) [20] are applied in various research fields.  
[PERSON] et al. proposed a hybrid optimization scheme for multiple thresholding by the criteria of OTSU, Gaussian function, and PSO algorithms [21]. [PERSON] et al. proposed a threshold segmentation algorithm based on 2D grey histogram and PSO for fire image segmentation [22]. An improved two-dimensional OTSU algorithm based on PSO was proposed to find the optimal threshold for lung parenchyma segmentation and extraction by [PERSON] et al. [23]. The multithresholding based on modified PSO (MMPSO) method by [PERSON] et al. was a PSO algorithm based on the new fitness function and OTSU method or multilevel thresholding [24]. [PERSON] et al. used Kapur entropy and OTSU combined with GWO algorithm to apply the multithreshold problem [25]. [PERSON] et al. proposed a multilevel threshold optimization method based on GWO. These principles included chaotic logistic map, the opposition-based learning (OBL), differential evolution (DE) algorithm, and the disruption operator (DO) [26]. [PERSON] et al. proposed the WOA-TH, GWO-TH, and PSO TH algorithm [27]. [PERSON] et al. proposed a method combining two-dimensional OTSU image threshold segmentation and improved firefly algorithm [28].  
This paper proposes an improved GWO algorithm. There is an improved GWO algorithm for initial population selection and threshold selection. By combining the GWO algorithm with the three principles, multithreshold OTSU, Tsallis entropy, and differential evolution (DE) algorithms, an improved GWO algorithm based on the DE algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). Each principle has its own function. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy quickly calculates the fitness. Differential evolution can solve the local optimal solution of the GWO algorithm.  
Compared with existing PSO and GWO algorithms, the experimental results show the stability, accuracy, and convergence of the DE-OTSU-GWO algorithm. In addition, when compared with other algorithms, a convergence behavior analysis proves the high quality of the DE-OTSU-GWO algorithm.  
The rest of the paper is organized as follows. Section 2 describes an improved GWO based on the DE and OTSU algorithms. Section 3 presents the experimental results and analysis. Section 4 details an experiment and discusses the best method for straw coverage detection. The last section summarizes this article.