A Multi-Level Approach to Waste Object Segmentation

Wang, Tao; Cai, Yuanzheng; Liang, Lingyu; Dong-yi, YE

doi:10.3390/s20143816

Cited by 48 publications

(15 citation statements)

References 77 publications

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“…Proença and Simões [ 37 ] presented TACO for litter segmentation with the presence of indoor and outdoor scenes. In the same vein, MJU [ 33 ] is another dataset for segmentation; however, unlike TACO, this dataset contains only indoor images with people holding the litter instances in their hands. For its part, Aquatrash [ 49 ] is an alternative dataset composed of a subset of images from TACO [ 37 ].…”

Section: Plastopol Datasetmentioning

confidence: 99%

“…Figure 1 provides examples where complex natural backgrounds and the presence of different kinds of litter complicate the detection task. Currently, there exist some litter datasets; however, most of them were built in controlled setups, i.e., with only one instance of litter per image [ 33 , 34 ] or taken in indoor scenarios for recycling [ 35 , 36 ]. Approaches developed based on such image collections cannot be generalized for real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Litter Detection with Deep Learning: A Comparative Study

Córdova

Pinto

Hellevik

et al. 2022

Sensors

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Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

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Section: Plastopol Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Litter Detection with Deep Learning: A Comparative Study

Córdova

Pinto

Hellevik

et al. 2022

Sensors

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show abstract

“…Steps to be followed from this study should advance towards a model of large-scale automation in the process of separating materials that would significantly increase the amount and suitability of recycled material. Currently there are already academic studies focusing on the development of robots that can make such classification [44], or those that applying deep learning techniques in the automation of the process [45], and even applied to agricultural policy, undoubtedly affecting sustainability [46]. As indicated by [47] "gain environmental, energy, and economic benefits" or [48] "the important role of consumers in circular economy business models through the examination of consumers' acceptance of recycled goods", this system allows automatic and individual recycling; and as shown by the study of [49] this recycling can even help convert waste into energy.…”

Section: Discussionmentioning

confidence: 99%

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

Alonso

Forradellas

Morell³

et al. 2021

Sustainability

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The great advances produced in the field of artificial intelligence and, more specifically, in deep learning allow us to classify images automatically with a great margin of reliability. This research consists of the validation and development of a methodology that allows, through the use of convolutional neural networks and image identification, the automatic recycling of materials such as paper, plastic, glass, and organic material. The validity of the study is based on the development of a methodology capable of implementing a convolutional neural network to validate a reliability in the recycling process that is much higher than simple human interaction would have. The method used to obtain this better precision will be transfer learning through a dataset using the pre-trained networks Visual Geometric Group 16 (VGG16), Visual Geometric Group 19 (VGG19), and ResNet15V2. To implement the model, the Keras framework is used. The results conclude that by using a small set of images, and thanks to the later help of the transfer learning method, it is possible to classify each of the materials with a 90% reliability rate. As a conclusion, a model is obtained with a performance much higher than the performance that would be reached if this type of technique were not used, with the classification of a 100% reusable material such as organic material.

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“…A multi-level approach (16) was introduced for segmenting the waste objects. First, the scene-level segmentation was applied to capture the long-range spatial contexts and create a primary coarse segmentation.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced segmentation network with deep learning for Biomedical waste classification

Mythili¹,

Anbarasi²

2021

IJST

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Objective: To maximize the accuracy of classifying the medical wastage, an Enhanced Segmentation Network (EnSegNet) with Deep Neural Network-Trash Classification (EnSegNet-DNN-TC) is proposed in this article. Methods: Initially, a core trainable segmentation network called SegNet framework is proposed which uses the Encoder-Decoder Network (EDN) and a pixel-wise classification layer for image segmentation. The decoder is used to upsample its low-resolution input feature maps via max-pooling. Also, SegNet uses fewer parameters for training. The uncertainty inherent to the EDN is modeled by the Bayesian functions to segment the input images. But, this SegNet can sample a limited amount of pixels in the images. Hence, an EnSegNet is proposed that uses Content-Sensitive Sampling (CSS) to sample more pixels in the data-sparse regions and fewer pixels in data-dense regions. Once the segmentation is completed, the DNN is applied for classifying the wastage using the segmented images. Findings: The experimental results show that the EnSegNet-DNN-TC framework achieves 88% accuracy compared to the DNN-TC for considering 100 images of different categories of biomedical wastes from the trash image dataset.

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A Multi-Level Approach to Waste Object Segmentation

Cited by 48 publications

References 77 publications

Litter Detection with Deep Learning: A Comparative Study

Litter Detection with Deep Learning: A Comparative Study

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

Enhanced segmentation network with deep learning for Biomedical waste classification

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