# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 2 Related Works
### Research on Garbage Detection and Classification  
Accurate garbage identification will greatly improve the automation and intelligence of cleaning vehicles. Earlier researchers have used RFID readers, load cells, gas sensors, etc., to implement garbage classification [4]. [PERSON] et al. [5] designed a dry and wet garbage sorting bin based on RFID readers with an Android application, where the public needs to display RFID tags to throw the garbage into the bin. However, this approach is not applicable to the sweeper scenario. Nowadays, with the rapid development of deep learning techniques, increasing amounts of researchers are trying to accomplish image processing problems using CNN, VGG, ResNet, UNet++, etc. [PERSON] et al. [6] proposed a robotic system using CNN for detecting garbage on the ground. After detecting the garbage, the location of the garbage is calculated and communication sharing is completed with the microcontroller controlling the robot arm. [PERSON] et al. [7] proposed a lightweight feature fusion single short multi-box detector (L-SSD) algorithm to achieve intelligent garbage classification and recognition, which solves the problem of the small size of garbage and low image resolution of garbage in garbage classification. ResNet-101 is used instead of VGG16 to achieve more accurate detection in the training set. [PERSON] et al. [8] proposed deep supervision UNet++ to solve the problem of road garbage classification and semantic segmentation; this method can directly identify the type of garbage and occupied ground area with an MIoU (mean intersection over union) of \(76.73\pm 0.11\), which greatly improves the accuracy of garbage segmentation compared with advanced methods.  
In addition, the YOLO family of algorithms is gradually becoming the leading computer-vision-based target detection and classification method [9]. Deep learning methods such as R-CNN (region-based CNN) typically use a two-stage target detection model that generates and selects regions of interest in the first and second stages, sends region suggestionsdown and uses convolutional neural networks for target classification and bounding box regression. Such models have high accuracy but are less real-time and less efficient [10]. The YOLO family of algorithms treats target recognition as a simple regression problem by creating a feature map of the input image and learning the category probabilities and the bounding box coordinates of the entire image, allowing the algorithm to run only once. Such a model is much faster than a two-stage target detector, although with reduced accuracy, and is commonly used for real-time target detection tasks [11]. A number of scholars have applied the improved YOLO algorithm to garbage detection and classification. [PERSON] et al. [12] trained the YOLOv3 network widely used in the field of high-speed accurate object detection on a floating garbage dataset to achieve accurate real-time garbage detection. To improve the anti-interference capability, a visual steering control method based on a sliding mode controller was proposed. [PERSON] et al. [13] proposed a YOLOX for detecting seventeen types of garbage, and after the camera detects the garbage, it automatically identifies and analyzes the type and specific location of the garbage and is automatically picked up by a mechanical jaw; the test results show that the \(mAP_{0.50.95}\) can exceed 97%.