# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 1 Introduction  
Apple is the fruit with the largest economic value and one of the few internationally competitive fruits in China [1]. In recent years, the aging of the population has resulted in a large shortage of agricultural labor. The use of smart engineering technology in the agricultural production process can help improve agricultural productivity [2]. Therefore, the research on and application of apple picking robots are of great significance to reduce the labor intensity of agricultural practitioners, free up agricultural labor and improve apple production. Detection methods for target apples on trees have been studied since the 1970s. Currently, the main methods to solve this problem mainly focus on traditional image processing algorithms, machine learning algorithms, and in recent years, on the fast developing deep learning technology. Traditional algorithms mostly make use of the red and green color differences between apples and background images, combined with different segmentation or edge detection algorithms, to achieve target detection. [PERSON] et al. used the normalized chromatic aberration method for fruit segmentation, and the proposed method was used to detect fruit pictures under different light brightness conditions. Finally, the randomcircle method was used to label the target fruit. The number of apples was taken as the unit to calculate the evaluation index, and the recognition rate reached 92% [3]. [PERSON] et al., proposed to use an optimized color difference method to segment apples and other fruits, and finally detect fruits through a random circular Hough transform [4]. [PERSON] et al., proposed using the chromatic aberration model and improved OTSU method for image segmentation. Taking the number of apples as the unit to calculate the evaluation index, the accuracy rate is 84.7% [5]. [PERSON] et al., proposed to segment right images in aCDR fusion space, and proposed an improved three-point circle determination method to identify overlapping fruits based on the segmentation results. Taking pixels as units to evaluate algorithm performance, the accuracy rate is 82.02% [6]. Machine learning is a common research hotspot in the field of artificial intelligence and pattern recognition. In recent years, it has been widely applied in the fields of medicine, aerospace, industry and so on [7; 8; 9; 10]. On the basis of ordinary image processing, researchers have combined important algorithms in the field of machine learning and proposed different feasible algorithms. For example, [PERSON] et al. fused the segmentation results obtained by the support vector machine (SVM) classifier based on color features and shape features to identify and mark fruit parts. Twenty pictures were tested, and the accuracy rate was 90.8% based on the number of pictures [11]. With the continuous development of research, deep learning technology has become a hot spot in the research of image recognition like [12; 13; 14; 15; 16], including apple fruit targets in natural scenes due to its high detection accuracy. [PERSON] et al., fused a K-means clustering algorithm and GA-RBF-LMS neural network to identify and detect 179 apple fruits, and the accuracy rate calculated by the number of apples was 96.95% [17]. [PERSON] et al., tested 487 test pictures by adopting the improved Lenet convolutional neural network apple target recognition model, and calculated the evaluation index by taking the number of apples as a unit, and the comprehensive recognition rate reached 93.79% [18]. [PERSON] et al., proposed apple location method based on the You Only Look Once (YOLOv3) deep convolutional neural network. The accuracy rate calculated by the number of apples is 97% [19]. [PERSON] et al., proposed the YOLOv3 and the YOLOv5 algorithms, where the YOLOv3 algorithm, supplemented by the described pre- and post- processing procedures, quite precisely detects both red and green apples.