Autonomous Trash Collector Based on Object Detection Using Deep Neural Network

Hossain, Shamima; Debnath, Bidya; Anika, Adrita; Junaed-Al-Hossain, Md.; Biswas, Sabyasachi; Shahnaz, Celia

doi:10.1109/tencon.2019.8929270

Cited by 24 publications

(6 citation statements)

References 3 publications

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“…The network was trained using a dataset that was created by researchers, and it contains four different categories of items. The findings of the experiments reveal that the upgraded version of the YOLOv4 method that was proposed has better detection performance compared to the YOLOV4 algorithm that was used initially, and that it has created adequate generalization performance in a variety of various sorts of trash, similar waste detection research for robotics applications was conducted by [61].…”

Section: A Waste Detectionmentioning

confidence: 93%

A Survey on Waste Detection and Classification Using Deep Learning

Abdu

Noor

2022

IEEE Access

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Waste or trash management is receiving increased attention for intelligent and sustainable development, particularly in developed and developing countries. The waste or trash management system comprises several related processes that carry out various complex functions. Recently, interest in deep learning (DL) has increased in providing alternative computational techniques for determining the solution to various waste or trash management problems. Researchers have concentrated on this domain, and as a result, significant research has been published, particularly in recent years. According to the literature, a few comprehensive surveys have been done on waste detection and classification. However, no study has investigated the application of DL to solve waste or trash management problems in various domains and highlight the available datasets for waste detection and classification in different domains. To this end, this survey contributes by reviewing various image classification and object detection models, and their applications in waste detection and classification problems, providing an analysis of waste detection and classification techniques with precise and organized representation and compiling over twenty benchmarked trash datasets. Also, we backed up the study with the challenges of existing methods and the future potential in this field. This will give researchers in this area a solid background and knowledge of the state-of-the-art deep learning models and insight into the research areas that can still be explored.

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Section: A Waste Detectionmentioning

confidence: 93%

A Survey on Waste Detection and Classification Using Deep Learning

Abdu

Noor

2022

IEEE Access

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“…A mobile trash collector was developed in [8] with CNN, deep network architecture which can only collect trash in bin with its robotic arms. With their self-prepared garbage dataset, the trained model has a better prediction which was 0.96 for two classes (trash or not trash) of dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

YOLObin: Non-Decomposable Garbage Identification and Classification Based on YOLOv7

Jabed¹,

Shamsuzzaman²

2022

JCC

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In order to safeguard the biological framework on which human creatures depend for presence and make society feasible improvement, it is becoming increasingly important to classify garbage. In any case, individuals are not recognizable with the classification strategy, so it is troublesome for individuals to accurately get the classification of each kind of rubbish. A proper waste management system is a primary task in building a smart and healthy city. In arrange to direct individuals to classify garbage accurately, this paper proposes a strategy of rubbish classification and acknowledgment based on YOLOv7 which is a cutting edge real time object detector. Performance of this model is compared along with two other object detectors where Mask-RCNN achieved f-measurement of 85%, YOLOv5 achieved f-measurement of 95.1% and YO-LOv7 achieved f-measurement 95.9%. We have used non-decomposable multiclass garbage images which entails messy backgrounds with unwanted images as well. Four classes of non-decomposable garbage data namely chips packet, plastic bottle, polythene and image with multiclass garbage with 1000 images are prepared for our dataset. Our experimental models performed well in classifying garbage images with cluttered backgrounds. We compared our test results to previous studies in which the majority of the models were tested and trained using laboratory images. The test comes about illustrates that the classification framework features a sensible degree of accuracy and the segmentation recognition impact is way better within the case of pointby-point picture, which can proficiently and helpfully total the rubbish classification errand.

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“…The paper in [8] describes a small autonomous robot based on Arduino, that using only Dense Neural Networks [9] performs indoor cleaning. The remarks about being in a simplified domain are still valid, the test phase is performed on the floor of a house/industry, with mostly uniform backgrounds and big objects to be found, at low speeds.…”

Section: Trash Collecting Robotsmentioning

confidence: 99%

An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection

Donati,

Fontanini,

Tagliaferri

et al. 2020

Applied Sciences

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Road sweepers are ubiquitous machines that help preserve our cities cleanliness and health by collecting road garbage and sweeping out dirt from our streets and sidewalks. They are often very mechanical instruments, needing to operate in harsh conditions dealing with all sorts of abandoned trash and natural garbage. They are usually composed of rotating brushes, collector belts and bins, and sometimes water or air streams. All of these mechanical tools are usually high in power demand and strongly subject to wear and tear. Moreover, due to the simple working logic often implied by these cleaning machines, these tools work in an “always on”/“max power” state, and any further regulation is left to the pilot. Therefore, adding artificial intelligence able to correctly operate these tools in a semi-automatic way would be greatly beneficial. In this paper, we propose an automatic road garbage detection system, able to locate with great precision most types of road waste, and to correctly instruct a road sweeper in order to handle them. With this simple addition to an existing sweeper, we will be able to save more than 80% electrical power currently absorbed by the cleaning systems and reduce by the same amount brush weariness (prolonging their lifetime). This is done by choosing when to use the brushes and when not to, with how much strength, and where. The only hardware components needed by the system will be a camera and a PC board able to read the camera output (and communicate via CanBus). The software of the system will be mainly composed of a deep neural network for semantic segmentation of images, and a real-time software program to control the sweeper actuators with the appropriate timings. To prove the claimed results, we run extensive tests onboard of such a truck, as well as benchmark tests for accuracy, sensitivity, specificity and inference speed of the system.

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Autonomous Trash Collector Based on Object Detection Using Deep Neural Network

Cited by 24 publications

References 3 publications

A Survey on Waste Detection and Classification Using Deep Learning

A Survey on Waste Detection and Classification Using Deep Learning

YOLObin: Non-Decomposable Garbage Identification and Classification Based on YOLOv7

An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection

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