# A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
###### Abstract
Then, the mean, the standard deviation, and the coefficient of variation of the approximate refractive index and intensity distributions were used to construct the feature. Finally, they used the SVM for the identification of PET plastic bottles [15]. The first type of method can achieve better sorting results in structured application scenarios where objects are relatively homogeneous in shape and regular in distribution, relying on preset processes and parameters. However, due to its low robustness, the manually designed feature extractor has limitations when the detection object or working environment is complex. For the second type of detection method, the main difference between it and the first type is feature extraction. In contrast, deep learning can automatically extract features and has the ability to learn from large amounts of data. Therefore, it has significant advantages in image recognition and data processing [16]. Convolutional Neural Network (CNN) is the most commonly used deep learning technique. [PERSON] et al. listed and discussed the application of classification models based on CNN in plastic waste detection [17]. For example, [PERSON] et al. studied the performance of VGG16, ResNet50, MobileNet V2, and DenseNet121 for classifying municipal solid waste, and ResNet50 performed the best [18]. [PERSON] et al. added the self-monitoring module on the basis of ResNet18 to improve the model's ability to classify different wastes [19]. Such methods rely on CNN to automatically extract features, and the accuracy rate can reach more than 85%. They address the limitations of the first type of method, and it is confirmed that deep learning can effectively extract the features of plastics and accurately classify them. However, the classification model can only predict one label each time. It is suitable for classification tasks that contain only a single object on each image [20]. In challenges with the need for both classification and localization, object detection based on deep learning is considered as an advanced solution [21]. Currently, object detection has been applied in many scenarios, such as defect detection [22], agricultural product detection [23], face detection [24], and automatic driving [25][PERSON] [PERSON] et al. proposed a YOLOv1-based neural network model with Variational Autoencoder (VAE) for detecting recyclable waste such as cans, batteries, and plastic bottles [26]. [PERSON] et al. combined MobileNet and SSD for multiple waste detection [27]. [PERSON] et al. constructed a YOLO-WASTE multi-label waste classification model based on YOLOv4 to realize fast recognition and classification of multiple wastes, including waste glass, waste fabric, waste metal, waste plastic, and waste paper [28]. [PERSON] et al. trained YOLOv3 on the dataset containing six categories--plastic containers, plastic bottles, metal, cartons, paper containers, and glass--for detecting domestic waste [29][PERSON] These efficient object detection models can be used to detect various types of waste. Therefore, this paper adopts object detection to output the category and location information of plastics. [PERSON] et al. proposed a single-category detection method based on the Region Proposal Network (RPN) and VGG-16 model. The robotic arm grasped plastic bottles among multiple categories of waste conveyed by the conveyor belt according to the geometric center and long-side angle [30]. [PERSON] et al. proposed an [PERSON] waste recycling method based on an SSD detector. A robotic arm received location and category information and sorted objects on an electronic rotating turntable [31]. [PERSON] et al. proposed a solid waste sorting method based on Fast R-CNN. They used a height profile scanner to measure the height of the objects and sent the category, location, and height information to the robot to achieve sorting [32]. These methods confirm that object detection can effectively assist the robotic arm in sorting.