Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging

Autonomous detection and sorting of litter using deep learning and soft robotic grippers

Road infrastructure is one of the most vital assets of any country. Keeping the road infrastructure clean and unpolluted is important for ensuring road safety and reducing environmental risk. However, roadside litter picking is an extremely laborious, expensive, monotonous and hazardous task. Automating the process would save taxpayers money and reduce the risk for road users and the maintenance crew. This work presents LitterBot, an autonomous robotic system capable of detecting, localizing and classifying common roadside litter. We use a learning-based object detection and segmentation algorithm trained on the TACO dataset for identifying and classifying garbage. We develop a robust modular manipulation framework by using soft robotic grippers and a real-time visual-servoing strategy. This enables the manipulator to pick up objects of variable sizes and shapes even in dynamic environments. The robot achieves greater than 80% classified picking and binning success rates for all experiments; which was validated on a wide variety of test litter objects in static single and cluttered configurations and with dynamically moving test objects. Our results showcase how a deep model trained on an online dataset can be deployed in real-world applications with high accuracy by the appropriate design of a control framework around it.

Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review

Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.

Computer vision for solid waste sorting: A critical review of academic research

Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little attention has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are introduced and compared. The distribution of academic research outputs is also examined from the aspects of waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were unevenly distributed in different sectors such as household, commerce and institution, and construction. Too often, researchers reported some preliminary studies using simplified environments and artificially collected data. Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested researchers to train and evaluate their CV algorithms.

A robust framework combined saliency detection and image recognition for garbage classification

Using deep learning to solve garbage classification has become a hot topic in computer version. The most widely used garbage dataset Trashnet only has garbage images with a white board as background. Previous studies based on Trashnet focus on using different networks to achieve a higher classification accuracy without considering the complex backgrounds which might encounter in practical applications. To solve this problem, we propose a framework that combines saliency detection and image classification to improve the generalization performance and robustness. A saliency network Salinet is adopted to obtain the garbage target area. Then, a smallest rectangle containing this area is created and used to segment the garbage. A classification network Inception V3 is used to identify the segmented garbage image. Images of the original Trashnet are fused with complex backgrounds of the other saliency detection datasets. The fused and original Trashnet are used together for training to improve the robustness to noises and complex backgrounds. Compared with the image classification networks and classic target detection algorithms, the proposed framework improves the accuracy of 0.50% - 15.79% on the testing sets fused with complex backgrounds. In addition, the proposed framework achieves the best performance with a gain of 4.80% in accuracy on the collected actual dataset. The comparisons prove that our framework is more robust to garbage classification in complex backgrounds. This method can be applied to smart trash cans to achieve automatic garbage classification.

A deep learning approach based hardware solution to categorise garbage in environment

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

Classification technology of domestic waste from 2000 to 2019: a bibliometrics-based review

Waste classification is to reduce solid waste and its associated environmental pollution. This paper applied bibliometrics to assess publications related to classification technology of domestic waste from 2000 to 2019. A total of 466 publications were retrieved. The results showed the number of citations and papers increased rapidly. The major publication type regarding waste classification technology is article and English is the primary language for academic communication. The research is multidisciplinary and interdisciplinary, and its research directions are mainly divided into "Engineering," "Environmental Sciences Economics," and "Chemistry." It was identified that Waste Management (85) published most of papers in this topic. Meanwhile, China (93) contributed the most of publications, followed by the USA (42), France (40), Japan (36), and Italy (28). European countries are in the leading position in the study of garbage classification technology. Plastics and waste metals were the existing focus of waste classification technology, and waste identification and classification has become an important classification method. In addition, we also summarized the current mainstream technology progress and possible research challenges.

Plastics in the time of COVID-19 pandemic: Protector or polluter?

The COVID-19 pandemic has reemphasized the indispensable role of plastics in our daily life. Plastics in terms of personal protective equipment (PPEs) and other single-use medical equipment along with packaging solutions owing to their inherent properties have emerged as a life-savior for protecting the health and safety of the frontline health workers and the common citizens during the pandemic. However, plastics have been deemed as evil polluter due to their indiscriminate littering and mismanagement amid increased plastic usage and waste generation during this unprecedented crisis. This article reviews and assesses to dwell upon whether plastics in the time of pandemic are acting as protector of the public health or polluter of the environment. Considering the utilities and limitations of plastic along with its management or mismanagement, and the fate, an equitable appraisal suggests that the consumers' irresponsible behavior, and attitude and poor awareness, and the stress on waste management infrastructure in terms of collection, operation, and financial constraints as the major drivers, leading to mismanagement, turn plastic into an evil polluter of the environment. Plastic can be a protector if managed properly and complemented by the circular economy strategies in terms of reduction, recycle and recovery, and thereby preventing leakage into the environment. To safeguard the supply chain of PPEs, several decontamination techniques have been adopted worldwide ensuring their effective reprocessing to prioritize the circular economy within the system. Policy guidelines encouraging to adopt safer practices and sustainable technical solutions along with consumers' education for awareness creation are the need of the hour for preventing plastic to turn from protector with high utility to polluter.

Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review

The general aim of circular economy is the most efficient and comprehensive use of resources. In order to achieve this goal, new approaches of Industry 4.0 are being developed and implemented in the field of waste management. The innovative K-project: Recycling and Recovery of Waste 4.0 - "ReWaste4.0" deals with topics such as digitalisation and the use of robotic technologies in waste management. Here, a summary of the already published results in these areas, which were divided into the four focused topics, is given: Collection and Logistics, Machines and waste treatment plants, Business models and Data Tools. Presented are systems and methods already used in waste management, as well as technologies that have already been successfully applied in other industrial sectors and will also be relevant in the waste management sector for the future. The focus is set on systems that could be used in waste treatment plants or machines in the future in order to make treatment of waste more efficient. In particular, systems which carry out the sorting of (mixed) waste via robotic technologies are of interest. Furthermore "smart bins" with sensors for material detection or level measurement, methods for digital image analysis and new business models have already been developed. The technologies are often based on large amounts of data that can contribute to increase the efficiency within plants. In addition, the results of an online market survey of companies from the waste management industry on the subject of waste management 4.0 or "digital readiness" are summarized.

A robust classification algorithm for separation of construction waste using NIR hyperspectral system

To improve the utilization rate of construction waste, reduce processing costs, and improve processing efficiency, we used near-infrared hyperspectral technology to extract and classify typical construction waste types. We proposed the pythagorean wavelet transform (PWT) to get the characteristic reflectivity to avoid the redundancy of hyperspectral data. Compared with the results from the wavelet transform (WT), we were able to retain more detailed information, and we observed the enhancement of differences between different species. To adapt to the complex conditions present in actual situations and to improve our ability to distinguish similar spectrum, we extracted, in addition to the characteristic reflectivity, four potential features. After classified verification, we found out that the first derivative and the intrinsic mode function (IMF) were effective features. At the same time the random forest (RF) algorithm was best at identifying trend-features, and the extreme learning machine (ELM) was better at identifying amplitude-features. We proposed a complementary troubleshooting (CT) method for the online identification of construction waste. After using the ELM to identify the characteristic reflectivity, the RF was used to identify first derivative for supplemental verification, which reduced errors due to working conditions and improved the overall model robustness and correctness. The accuracy of proposed method can reach 100% in identifying 180 samples with 6 types including woods, plastics, bricks, concretes, rubbers and black bricks.