# Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification
## 1 Introduction
The performance of the object detector CNN depends on several parameters such as data augmentation, the CNN model, number of images in training/testing dataset, loss optimization, hyper-parameters adjusting, evaluation metrics, transfer learning, fine-tuning, etc. Thus, in order to develop an accurate and real-time architecture, a complex set of actions needs to be carried out. In addition to enhancing the capabilities of object detectors for waste identification and sorting in terms of precision and inference time, the paper presents an in-depth study on the performance of several artificial intelligence methods applied to trash classification. This paper focuses on object identification based on municipal waste images, more precisely the AI techniques used for localization and classification. The goal is to obtain a real-time and accurate CNN object detector which will be able to correctly classify and locate municipal waste, in order to be identified and picked up by the autonomous mobile robot system. A preliminary study in the area of waste sorting using deep learning has been performed by our team in a previous paper [6]. More precisely, pre-trained CNN architectures have been evaluated on a custom trash test dataset, created in under laboratory conditions, with a high degree of variance (high degree of transparency, uncommon positions, lower resolution, cropped or blocked images), in order to test the real generalization of the models outside the training and testing dataset. This previous research has shown that the most accurate model at inference time in terms of the confidence score obtained on the custom dataset is the Single Shot Detectors (SSD) architecture with MobileNetV2 base network pre-trained on the COCO (Common Objects in COntext) dataset and fine-tuned on the PASCAL Visual Object Classes (VOC) dataset. Based on these first results, this current research continues the study by training the best models on the TrashNet dataset using transfer learning and fine-tuning, data augmentation, regularization, and finding the optimum limits of the learning rate. In the literature, there are several research works developed in the area of waste sorting using deep learning. The first important results in waste sorting using deep learning were obtained in [7]. The authors developed a database for municipal waste, called TrashNet and used it to train two classifiers: one support vector machine (SVM) and a CNN very similar to the AlexNet model with the Softmax classifier. SVM performed better with an accuracy of 63%, while CNN did not learn well due to hyper-parameter setup. Following the results of this research, a better mean average precision of 68.3% has been obtained [8] using Faster R-CNN and augmentation of the dataset. In [9], a VGG-16 CNN has been used for training and a validation accuracy of 88.42% has been obtained, overfitting the CNN, where some adjustments of the hyper-parameters, architecture, or classification on the fully connected layers have been performed. Newer research on the TrashNet dataset has provided better results. For example, in [10], a Faster R-CNN object detector based on Inception V2 and pre-trained on the COCO dataset has been tested. The results showed a precision of 84.2% and a recall of 87.8%. Using DenseNet121 and Inception-ResNetV2, a test accuracy of 95% and 87%, respectively, has been obtained. In the same paper, the new model, RecycleNet, had shown 81% test accuracy [11]. In [12], the results showed a test accuracy of 93% using a pre-trained VGG-16 network. [PERSON] and [PERSON] used a ResNet50 network with an SVM classifier and obtained 87% accuracy [13]. In [14], the automation of Waste Sorting with Deep Learning has achieved 86%, but the best results, so far, are obtained by [15] using MobileNetV2 for feature extraction and an SVM classifier, with an accuracy of 98.7%. However, this paper has not been peer-reviewed yet.