# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 4 Results and Discussion
### Comparison and Analysis of Improved Salient Graph and Binary Graph Algorithm  
On 1036 test pictures, the saliency map generated by this method is further combined with the algorithm proposed in [3], the algorithm proposed in [4], the algorithm proposed in [5], and the aCbR fusion space [6] saliency detection algorithm (aCbR saliency detection) and for comparison, some of the processing results are shown in Figure 6. The algorithm proposed in [3] has a large difference in brightness between the fruits and the background, but the individual fruits are not complete. The algorithm proposed in [4] has a relatively complete fruit part, but the background part is brighter, and the brightness difference with the fruit part is small, which is not conducive to subsequent image segmentation. The basic color difference method [5] has a large difference in brightness between the fruit part and the background part, but the edge part of the fruit cannot be completely segmented. The LC saliency detection under the aCbR fusion space [6] is not bright enough for the fruit parts. It can be seen from the results that the method in this paper can accurately separate the apple fruit from the background part. In addition, the fusion-improved saliency detection method proposed in this paper identifies the part of the fruit with higher brightness and more significant. Based on the artificial ground truth (GT) map of manually labeling real apple fruits, the corresponding P-R curve and ROC are generated as shown in Figures 7 and 8. From the perspective of the PR curve, among the PR curves of each algorithm, the curve of the method in this paper is located above the curve of other methods, and is closer to the upper right corner. The curve of the algorithm in the article overlaps and intersects a small part of the curve of the algorithm proposed in [3], but through the balance point (that is, the intersection of the diagonal of the quadrant and the curve), it can be seen that the BEP value of the article algorithm is the largest one, so the result of the algorithm proposed in the article is better than the results of the other four comparison algorithms. In the PR graph, the PR curve of the article algorithm does not intersect the Y axis because the fruit part of the saliency map is too bright, the pixel value is generally close to 255, the  
Figure 5: Results of each stage of the proposed method. (**a**) Original image; (**b**) Fusion improvement salient graph; (**c**) Overlapped fruit separation results; (**d**) Target fruit detection (the white box in the target fruit labeling is the initial detection result, and the green box is the segmentation optimization result of DeepSnake instance). background part is too dark, and the pixel value is generally close to 0, so the curve recall rate is (0, 0.6026) There is no value in the interval. At the same time, it can be seen from the ROC that the curve of the algorithm in this paper is clearly on the upper left of the curve of other algorithms. Therefore, the article algorithm is better than other algorithms. After OTSU adaptive segmentation, mathematical morphology operations and hole filling processing to generate a binary image, and the K-means and GA-RBF-LMS neural network algorithm in the literature [8], the algorithm ((RG)/(R + G)) [3], algorithm (2R-GB) [4], basic color difference method [5], and fusion space [6] saliency detection (cCbR saliency detection + OTSU) segmentation result comparison is shown in Figure 9. Calculated corresponding F-measure values are shown in Table 2. Compared with the other five comparison algorithms, the fruit part of the method in this paper is more complete, with fewer noise points, closer to the GT map, and the F-measure value is the largest. The above qualitative and quantitative processing results show that the performance of the apple initial target detection method in this paper is better than the other five comparison methods. Figure 6: Comparison of various apple fruit detection algorithms.