# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 5 Experimental Analysis
### Analysis and Discussion  
In this section, the proposed APC algorithm is compared with some mainstream artificial intelligence algorithms, such as the artificial bee colony (ABC) [5; 41], fuzzy C-means (FCM) [9; 14; 27; 28], convolutional neural networks (CNNs) [14; 15; 16; 17; 34; 35; 36; 37], particle swarm optimization (PSO) [18; 40], ant colony optimization (ACO) [19; 21], genetic algorithm (GA) [22; 42], artificial fish swarm algorithm (AFSA) [23], and simulated annealing (SA) [26]. The algorithm comparison using the Pascal VOC 2012 dataset is shown in Figure 5, and the algorithm comparison using the Stanford background dataset is shown in Figure 6.  
The first column in Figure 5 contains the original images from the Pascal VOC 2012 dataset. The eight images have \(500\times 340\) pixels, \(500\times 375\) pixels, \(350\times 500\) pixels, \(500\times 343\) pixels, \(375\times 500\) pixels, \(375\times 500\) pixels, \(500\times 375\) pixels, and \(500\times 375\) pixels, respectively. The second column in Figure 5 shows the edge detection results for our proposed APC algorithm, and columns 3-10 in Figure 5 are the results of the ABC [5; 41], FCM [9; 14; 31; 32], CNN [14; 15; 16; 17; 34; 35; 36; 37], PSO [18; 40], ACO [19; 21], GA [22; 42], AFSA [23], and SA [26]. Figure 5 presents eight cases, including the bus, yacht, eagle, plane, bridge, telephone, computer operator, and building, and they sh algorithm can achieve similar effects in image edge detection to other artificial algorithms. The 5 th column in Figure 5 presents the results of a multi-layer convolutional neural network [14; 15; 16; 17; 34; 35; 36; 37]. This algorithm can achieve a good edge connection effect, but it is the most time-consuming of all algorithms. Other artificial algorithms can achieve good edge detection results with shorter calculation times, and are more suitable for fast and high-quality processing of image data. The comparative results, using the Pascal VOC 2012 dataset, show that our algorithm can successfully build a high-quality edge image, that is not inferior to other AI algorithms, through the evolution mechanism of an artificial plant community. Figure 6 shows more cases from the Stanford background dataset to provide a further comparison between the proposed algorithm and other artificial intelligence algorithms. Figure 6 gives eight cases, including the bungalow, yacht, Si-o-Seh Bridge, apartment house, streetscape, motorcyclist, red car, and horse feeder. The first column in Figure 6 contains the original images from the Stanford background dataset. The eight images have \(320\times 240\) pixels, \(320\times 240\) pixels, \(320\times 240\) pixels, \(320\times 213\) pixels, \(320\times 240\) pixels, \(320\times 240\) pixels, \(320\times 239\) pixels, and \(320\times 264\) pixels, respectively. Columns 3-10 of Figure 6 show the edge detection solutions of the different algorithms, including our proposed APC algorithm, ABC [5; 41], FCM [9; 31; 32; 14; 33], CNN [14; 15; 16; 17; 34; 35; 36; 37], PSO [18; 40], ACO [19; 21], GA [22; 42], AFSA [23], and SA [26]. The results presented in Figure 6 again verify that the proposed algorithm can achieve similar effects in image edge detection to other artificial intelligence algorithms. Because these test images from the Stanford background dataset have fewer pixels than those from the Pascal VOC 2012 dataset, the computational workload is greatly reduced, and all algorithms can complete the edge detection task in a shorter period of time. Among them, the computing time required for the CNN [14; 15; 16; 17; 34; 35; 36; 37] algorithm is significantly reduced,  
Figure 6: Algorithm comparison using the Stanford background dataset; ABC [5; 41]; FCM [9; 14; 31; 32]; CNN [14; 15; 16; 17; 34; 35; 36; 37], PSO [18; 40]; ACO [19; 21]; GA [22; 42]; AFSA [23]; SA [26]. but it is still the most time-consuming algorithm. Additionally, our APC algorithm can successfully complete the edge detection tasks and achieve a good and timely performance.