# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 4 Experimental Results
### Garbage Detection Experiments Based on YOLOv5 and PE  
This experiment uses the common garbage detection and obstacle dataset on the roadway. This dataset contains 14,964 pictures, of which there are 20 categories of GT frame labels, which are as follows: bag, book paper, can, paper box, traffic sign, roadblock, stone pier, etc. Since the experiment mainly detects the common garbage targets on the sidewalks of enclosed parks, only six of these categories are detected for garbage: leaf, tissue, paper, can, carton, and bottle. Only three of these categories are detected for obstacles: roadblocks, stone piers, and bicycles. Some of the images in the dataset are shown in Figure 13. After filtering the dataset and removing the images that do not contain the above objectives, 10,000 images are retained, of which 9000 and 1000 are in the training and validation sets, respectively.  
For comparison, we choose Faster RCNN [30], YOLOv3 [31], YOLOv4 [32] and our algorithm. We use time, precision, recall, and mAP (mean average precision) as the evaluation metrics of the model to measure the performance of the model. Time represents the time spent for testing 100 validation sets. Precision is the rate of correct predictions  
Figure 12: Experimental sweeping vehicle platform and actual scene.  
Figure 13: Selected images from the Obstacles and Spam dataset.  
among all results where the prediction is a positive sample, and here we focus on checking the accuracy of the most common garbage on the road, i.e., bottles. Recall is the rate of correct predictions among all positive samples. mAP is the mean pixel accuracy of the total category N, which measures how good the model is over all the categories. The comparison results of different network models are shown in Table 1.  
For the complex garbage pile, this experiment additionally uses a dataset open-sourced by [PERSON] [33], which contains 2561 images and 956 images containing garbage; the rest are non-trash images that are very similar to the trash in terms of various visual attributes. Therefore, this dataset cannot fully meet the needs of the road garbage scenario and needs to be collected by ourselves. As shown in Figure 13, for the various types of garbage images used in this experiment, the first row is the image of a single garbage, the second row is part of the images in the SpotGarbage dataset, and the third row is the self-acquired images of road garbage.  
For comparison, we choose UNet++ [34], DUNet++ [8], YOLOv3+Self-Cluster and our algorithm YOLOv5+PE. We still use time, AP (average precision), and MIoU (mean intersection over union) as model evaluation metrics to measure the performance of the model. Time represents the time spent for testing 100 validation sets. AP is the pixel accuracy, where the ratio of correct pixels is calculated on a per-category basis. MIoU is the ratio between the intersection and the union of the ground truth and algorithm-predicted segmentation of the garbage region. Performance comparisons of the YOLOv5+PE algorithm with different network models are shown in Table 2. It can be seen that the YOLOv5+PE algorithm has a significant increase in detection accuracy, although the detection time is longer.