# Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification
## 1 Introduction  
Municipal waste sorting is becoming an important matter in cities across the European Union (EU) and beyond. The most recent data showed that in the EU, 786 million tones of waste, excluding major mineral waste, were generated in 2016, equivalent to 35 % of the total waste generated. This means that in relation to population size, the EU generated, on average, 1.8 tones per inhabitant of waste, excluding major mineral waste, in 2016 [1]. Roughly half of the waste (52.6 %) went through different recovery operations: recycling (36.7 % of the total recovered waste), backfilling (10.1 %), or energy recovery (5.8 %). The remaining 47.4 % was landfilled, incinerated without energy recovery, or disposed. By 2020, the recycling of waste materials such paper, metal, plastic, and glass from households and possibly from other origins was forecasted to a minimum of overall 50% by weight. By 2035, the target is increased to 65% [2]. The latest EU report, from 2016, shows that the average amount of municipal waste that has been recycled was 45%. One method to maximize recycling is to use autonomous systems which must accurately sort the waste in a short amount of time. One type of autonomous system is the mobile robot used for collectingwaste from the ground, which has been developed by our team in collaboration with mechatronic and computer science researchers [3]. The objects that make up the trash are detected using a camera mounted on top of the robotic system and are processed for identification using computer vision and deep neural network models. The convolutional neural network (CNN) model with the best performance will be embedded on an autonomous mobile robot equipped with an \(x\)-\(y\)-\(z\) robotic manipulator (Festo planar \(x\)-\(y\) surface gantry plus a vertical electrical slide on \(z\) axis), supplied only by DC 24 V for increased autonomy, the end-effector being a Festo electric parallel gripper. The gripper is intended to collect waste objects from the ground and to manipulate them to a box container placed into the mobile robot. In order to identify the municipal waste objects to be collected, an image acquisition and processing system is needed. The image acquisition can be performed using various cameras; in our case, two different designs are tested in parallel: a Festo specialized camera type SBPA-R2-C-U3-2E2A-CS with a CECC-X-M1-MV camera controller and an Omni Vision OV5647 camera with a Raspberry Pi 3+ controller, with a dual-core processor of 1 GHz frequency and 1 GB RAM. In order to reduce the inference time, a USB neural processing device (Neural Compute Stick v2) is added to the second design of the image processing system. There are some other research works that focus on developing robotic systems for autonomous collection and classification of trash [4] or for identification of construction waste such as nails and screws [5]. The implementation of a neural network model on the mobile robot and other aspects of this autonomous sorting framework is not the scope of the paper. In order to obtain an efficient deep neural network model, the research focuses on boosting the performance of pre-trained Convolutional Neural Network Object Detectors, in terms of precision, generalization, and detection speed, using loss optimization methods, data augmentation, or asynchronous threading at inference time. The model with the best performance will be embedded in a robotic application for waste sorting and collecting from the ground. The images in the dataset are pre-processed at the training time in order to improve generalization and to extend the dataset. After pre-processing, the images are transferred to the object detectors feature extractor CNN. The proposals of the feature extractors will be the input for the box predictor modules, which will be responsible for localization and classification at different feature map sizes.