AquaVision: Automating the detection of waste in water bodies using deep transfer learning

Panwar, Harsh; Gupta, Pradeep Kumar; Siddiqui, Mohammad Khubeb; Morales-Menéndez, Rubén; Bhardwaj, Prakhar; Sharma, Sudhansh; Sarker, Iqbal H.

doi:10.1016/j.cscee.2020.100026

Cited by 77 publications

(28 citation statements)

References 17 publications

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“…The datasets widely used for object detection evaluation and benchmarking such as the COCO dataset [21] typically contain objects such as vehicles, animals, and household objects devices whose relative size is on average larger than ones found in aerial imagery. Datasets containing trash contain mostly images taken from the point of view of a pedestrian [4] or are small in size [54]. The comparison of relative object size distributions in the TACO and UAVVwaste datasets is shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…System for litter and trash detection for coastal areas are described e.g., in [51,52], with the first being human-operated with the purpose for longitudinal study, and the latter relying on cloud resources for processing. Marine litter detection problem is also brought up in [53], but the paper focuses on data generation and deals with underwater detection, while [54] deals with trash detection in water bodies using drones. The latter also introduces an application-specific dataset (AquaTrash), but it is of relatively small size.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

et al. 2021

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Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

et al. 2021

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“…For example, Asad and Andersson (2020) described a ‘robotic arm’, set up with AI-based technology which can segregate and define the type and composition of plastic waste; hence, contributing a vital step towards the waste handling process. Interestingly, Panwar et al (2020) developed an AI-based model – AquaVision – that can detect discarded plastic waste in waterbodies or oceans with greater accuracy. Also Kumar et al (2018) used different AI-based techniques in developing a tool which can project the plastic waste generation amount for a given area with higher precision rate.…”

Section: Future Recommendationsmentioning

confidence: 99%

SARS-CoV-2 pandemic-induced PPE and single-use plastic waste generation scenario

et al. 2021

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The SARS-CoV-2 pandemic has demonstrated both positive and negative effects on the environment. Major concerns over personal hygiene, mandated and ease in lockdown actions and slackening of some policy measures have led to a massive surge in the use of disposable personal protective equipment (PPE) and other single-use plastic items. This generated an enormous amount of plastic waste from both healthcare and household units, and will continue to do so for the foreseeable future. Apart from the healthcare workers, the general public have become accustomed to using PPE. These habits are threatening the land and marine environment with immense loads of plastic waste, due to improper disposal practices across the world, especially in developing nations. Contaminated PPE has already made its way to the oceans which will inevitably produce plastic particles alongside other pathogen-driven diseases. This study provided an estimation-based approach in quantifying the amount of contaminated plastic waste that can be expected daily from the massive usage of PPE (e.g. facemasks) because of the countrywide mandated regulations on PPE usage. The situation of Bangladesh has been analysed and projections revealed that a total of 3.4 billion pieces of single-use facemask, hand sanitizer bottles, hand gloves and disposable polyethylene bags will be produced monthly, which will give rise to 472.30 t of disposable plastic waste per day. The equations provided for the quantification of waste from used single-use plastic and PPE can be used for other countries for rough estimations. Then, the discussed recommendations will help concerned authorities and policy makers to design effective response plans. Sustainable plastic waste management for the current and post-pandemic period can be imagined and acted upon.

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“…For example, Serra-Toro et al [39] tended to monitor water quality by recognizing the fish swimming behavior from video images. To promote learning-based waste detection in water bodies, a dataset (i.e., AquaTrash) [40] was developed based on existing TACO dataset [41] to assist in protecting water sources. Benefiting from the strong learning capacity of deep models, an extension of YOLOv3 [42] performs well in effective detection of vision-based water-surface garbage.…”

Section: Detection Of Water-surface Targets In Maritimementioning

confidence: 99%

Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation

Guo

et al. 2021

Journal of Advanced Transportation

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The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy.

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AquaVision: Automating the detection of waste in water bodies using deep transfer learning

Cited by 77 publications

References 17 publications

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

SARS-CoV-2 pandemic-induced PPE and single-use plastic waste generation scenario

Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation

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