# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 3 Proposed Methodology
### Garbage Distribution Sensing  
#### 3.1.1 Discrete Object Recognition  
The YOLO family can extract category information. Among them, YOLOv5 has a good performance in traffic target detection, and the algorithm only needs GPU training to generate a low-cost, robust, real-time, high-quality, and convincing target detection model. YOLOv5 consists of 4 parts: input, backbone, neck, and head. In this paper, we use YOLOv5 to recognize the type and exact location of specific obstacles and individual garbage. Many objects in the roadway, such as leaves or bottles, tend to be distributed within close proximity, thus forming a group or cluster. Treating them as discrete objects would only increase the number of nodes to be passed in the TSP, leading to a steep increase in computation cost. Therefore, as shown in Figure 3, we introduce the group in which neighboring targets of the same type are treated as a cluster so as to lower computational cost. Further, in practical scenarios, the irregular distribution of complex garbage piles often causes stacking of garbage, which leads to garbage higher than the brush height of the sweeper, making it impossible for the sweeper to complete the sweeping work. For this kind of situation, we will process the LIDAR point cloud information. LIDAR can emit laser beams to detect the position, velocity and other characteristic quantities of a target. As shown in Figure 4, which shows the process of converting our radar point cloud into a local elevation map, the gray rectangle is the vehicle model of this vehicle, and the orange cube is the recognized elevation terrain. Once the height of the garbage pile is detected to be higher than the threshold value, this pile of garbage is determined as an obstacle and the avoidance strategy is executed. Figure 3: The mind map of the designed unmanned sweeper vehicle sweeping method in this paper. The inverse perspective transformation is the inverse process of perspective imaging, which acquires a bird's eye view of the road plane by back-projecting the image coordinates to the spatial coordinates. Ref. [26] proposed the inverse perspective transformation based on the pitch angle and yaw angle of the vanishing point, which finally realizes the solution of the position of the image plane corresponding to the road plane captured during the working process of the sweeper. In this paper, the inverse perspective transformation is used to eliminate the perspective effect and obtain the top view of the real road surface, and the YOLOv5 algorithm completes the recognition of single garbage and obstacles. As shown in Figure 5, it is the result of recognizing obstacles and single items of garbage with the YOLOv5 algorithm after inverse perspective transformation of multiple initial images. YOLOv5 is used to recognize a single garbage target. In order to make the recognition information more qualitatively dense and reduce the planning redundant points, we further fused the feature points for recognition by a GMM-based skeleton feature downscaling method, which maps the skeleton features of the garbage into a two-dimensional plane and makes the feature points more characteristic. GMM is a K-means-like model that quantizes things precisely with Gaussian probability density function [27]. Firstly, we discretize the recognized sweeping targets, i.e., we sparsify the features of a single target by selecting some random points in the target box. The random points in the perceptual domain form a set, and the region formed by similar targets can often be characterized by independent normal distributions, so we adopt the GMM model to characterize each discrete region. As shown in Figure 6, the initial image is a Gaussian mixed distribution, and the accurate detection and target localization of garbage is accomplished. Figure 4: Mapping local terrain to elevation terrain, where gray rectangle is the vehicle model of this vehicle, and the orange cube is the recognized elevation terrain.