Intelligence, Informatics and Infrastructure, Volume 5, Issue 1, 2024

Comprehensive Analysis ofOn-Site Riparian Waste Pollution:
A Case Study on the Hyakken River BasinShijun PAN1, Keisuke YOSHIDA2, Takashi KOJIMA3
1Member of JSCE, Doctoral Student, Graduate School of Environmental and Life Science, Okayama University(Tsushima-naka 3-1-1, Kita-ku, Okayama 700-8530, Japan)
E-mail: p4b36znn@s.okayama-u.ac.jp (Corresponding Author)
2Member of JSCE, Associate Professor, Graduate School of Environmental and Life Science, Okayama University
3Senior Researcher, TOKEN C. E. E. Consultants Co., Ltd

Environmental degradation due to waste pollution (i.e., abandon artificial objects in the naturalenvironment) is a growing concern that demands comprehensive understanding and effective solutions.
This paper discussed the intricate issue of waste pollution, focusing on the Hyakken River Basin. This study
presents of the waste pollution under long-term sunshine and/or water flow brush-up in the area,emphasizing the transformative effects of these environmental factors on the waste pollution. To
substantiate this findings, the authors utilized smartphones to capture high-resolution images. To train theseimages in a YOLOv8 model, the datasets were meticulously annotated using roboflow, incorporating
advanced data augmentation techniques. Subsequent testing involved the application of the model tosmartphone videos from distinct sections of the field. Engaging actively in the cleanup process alongsidededicated volunteers, the authors successfully cleared the riparian area, providing a firsthand account of thepositive impact of collaborative efforts. The on-site waste pollution after long-term in the naturalenvironment is not similar to commercial goods (i.e., color and shape). Several models (i.e., pre-trained andcustom-trained) have been tested with the Hyakken River Basin Wild Dataset. Derived from the results,
custom-trained YOLOv8n-seg model has better results. Comparing with the SAM result without specifiedlabel, custom-trained YOLOv8n-seg model also has potential of improving. Looking ahead, the authors
envision the utilization of this dataset as a valuable tool for supporting both volunteers and government
staff in waste amount analysis. By contributing to environmental protection initiatives, this research aspires
to pave the way for informed decision-making and sustainable solutions in the ongoing battle against waste
pollution.
Key Words: Instance Segmentation, Riparian Waste Detection, River Monitoring, Smartphoneprevious research4) were settled on-site manually,that have some different features comparing with thereal waste pollution in the natural environment.
This study embarks on an exploration of thedynamics of waste pollution in this region, aiming tooffer a comprehensive understanding of its
multifaceted nature. The utilization of smartphones
allowed the authors to capture high-resolution
images, forming the foundation for an image-based
dataset. During the process of the on-site smartphonedata collection, the drone-derived images around thesame area have also been collected. Because of thetime limitation, in this research, the authors justfocused on the dataset derived from the on-site
smartphone-based images.
Owing to the limitation of the on-site data
accumulations, the authors considered that the1. INTRODUCTION
These years, the riparian environment around theHyakken River, Japan has become much worse than
before with a persistent environmental challenge:
waste pollution. And at the same time, the waste
pollution problem is not just in Japan, also existed
around the worldwide1), 2), 3). Even there are severaldatasets1), 2), 3) that have been published online, thedataset related to long-term waste pollution in theriparian environment is also very rare.
Based on the mentioned practical problems, theauthors trained the AI models, and applied themodels to detect the waste pollution4) in theenvironmental management. But the targets in the98

Intelligence, Informatics and Infrastructure, Volume 5, Issue 1, 2024

Fig.1 Bird’s eye view images of the study site (left-side from Google map, collected at 20th, Sep, 2022; middle- and right- sides from
DJI Mavic2, collected at 17th and 29th, Mar, 2022 before and after the waste pollution collection by volunteers, respectively)
and positions of the data-collection points derived from the GPS data in each on-site images.

aspire to contribute not only to academic discourse
but also to the practical tools available forenvironmentalstewardship.
Envisaging
theapplication of this dataset as a resource for volunteers
and government staff involved in waste amountanalysis, the authors seek to catalyze a paradigm shift
in approaching waste pollution. In doing so, thisresearch emerges as a pivotal endeavor in theongoing quest for informed decision-making andsustainable environmental solutions.

application of the data augmentation technology toincrease the data amount is necessary. Thus,
leveraging roboflow5) (i.e., an open source online
website for data processing) for dataset annotationand incorporating data augmentation techniques toensure an examination of the waste landscape.
Considering of applying the state-of-the-art open
source models on the dataset examination, theimplementation of the YOLOv86) model (i.e., a
cutting-edge, state-of-the-art model for computer
vision tasks) and SAM7) (i.e., segment anythingmodel, performs pixel-level classification by
categorizing pixels into various classes. These
classified pixels correspond to different objects orregions within an image.) were considered, further
validated through rigorous testing with smartphone
videos from distinct sections of the field.
Beyond the realm of data analysis, thisinvolvement in on-the-ground cleanup efforts
alongside dedicated volunteers provided a tangible
connection to the impact of waste pollution on theriparian area. This firsthand experience serves as apoignant backdrop against which the gravity of thewaste pollution issue is vividly portrayed,challenging preconceived notions that the conditionof waste on-site is similar to waste samples for theprevious research.
As the authors present the findings, the authors

2. STUDY SITE AND METHODS(1) Study site
Fig.1 showed the bird’s eye view of the Hyakken
River basin in Okayama City, Japan. The Hyakken
River is an artificial river that was constructed in the17th century as a floodway of the Asahi River toprevent flooding of Okayama Castle. The river has aunique structure that combines a low stoneembankment (arate) and a drainage gutter gate at its
mouth, which allows water to overflow and drain into
the sea during floods. The river also serves as anirrigation canal for the surrounding farmland. In theFig.1, the authors also showed the positions of thedata-collection points (i.e., 24 pins in the map), thatare located in the middle of the tidal zone with99

Intelligence, Informatics and Infrastructure, Volume 5, Issue 1, 2024

been trained on various datasets (e.g. the COCO
dataset, one of the most widely used benchmarks forobject detection and segmentation). In this research,
the authors also trained YOLOv8 model using thedataset derived from the on-site images to comparewith the results derived from the pre-trained version.
As a comparing reference of the mentioned twomodels (i.e., custom- and pre-trained YOLOv8model), the segmented mask results derived fromSAM also have been in consideration. The reason ofchoosing this model is derived from its extensive
training dataset comprising 11 million images and 1.1
billion masks and zero-shot performance across avariety of segmentation tasks, even SAM cannot
identify the specific classes using “Everythingmode”. If SAM also cannot extract the objects fromthe background, these objects are comparatively
difficult to detect only using the image.

Fig.2 Details of the cameras in Redmi 9s (MP: mega pixel).

(4) Workflow
Fig.3 introduces a workflow designed for a waste
dataset collection and application, that can support
the other researchers in the data collection as areference manual. There are simply four steps
included in this workflow: Data collection, Dataannotation, Dataaugmentation and Data
combination, individually.
1. Data Collection from Smartphone Devices: The
primary stage is data acquisition. Leveraging the48MP main camera in the smartphone, the authors
gathered data (i.e., images) directly from a near
distance to the objects using this portable device.
2. Annotation via Roboflow's Smart Polygon
Function: The next phase, the authors transited into
the realm of annotation, a step in elucidating thecollected data. Roboflow, an annotation generation
platform, with its cutting-edge Smart Polygonfunction (i.e., powered by the segment anythingmodel from Meta AI), emerges as the tool of choicefor this task. As performed in Fig.4, there are four
steps for annotating the objects, and the annotations
files can be outputted automatically.
3. Data Augmentation for Image Preprocessing:Embarking on the quest for enhanced dataset quality,
the authors applied data augmentation techniques on
the preprocessed images. This stage aims to diversify
and fortify the dataset, making it resilient to various
real-world scenarios as shown in Fig.5. Consideringof using un-color-change data augmentation methods
only, Fig.6 displayed the order of how to increase thedataset in this research.
4. Compilation of HRB-WD (Hyakken RiverBasin Waste Dataset): The researchers culminate in
the creation of the Hyakken River Basin Waste
Dataset (HRB-WD), that include the corresponding
images, annotations.

Fig.3 Work flow of collecting the HRB-WD.

approximate 4.2 km distance from the river mouth.
(2) Device
In this research, data collection was derived froman Android phone called Redmi 9s, that is a low-cost
smartphone from Xiaomi that comes with fourcameras as shown in Fig.2 (i.e., 119° ultra wideangle, 48MP main camera, macro lens and depthsensor). The images were taken by the main camera,that is a 48MP high-resolution sensor with a brightf/1.79 lens. The size of the image is 4000 pixels ×3000 pixels, width and height, individually.
(3) Model
YOLOv8 is a model developed by Ultralytics,
YOLOv8 is faster and more accurate than previous
versions of YOLO in this company, and can handle
various computer vision tasks (i.e., Object Detection
and Instance Segmentation) with a single model. In
this model, there are several sizes (i.e., n, s, m, l andx). YOLOv8n is the smallest version, optimized forlower speed but with lower detection performance.
YOLOv8x achieves remarkable accuracy whilehandling a wide range of objects. Considering of how
accurate the YOLOv8 model can extract the waste
pollution from the complicated backgrounds, rather
than only bounding-box from Object Detection, themasks aided with bounding-boxes from InstanceSegmentation are more suitable for this research.
YOLOv8 model has a pre-trained version, that has100

Intelligence, Informatics and Infrastructure, Volume 5, Issue 1, 2024

Fig.4 Process of annotating the object with roboflow: 1. Selecting the toolbox; 2. Clicking on the object; 3. Generating the masks;
4. Positions derived from the mask.

Fig.5 On-site individual waste pollution samples, that included but not limited in plastic bags, PET bottles that have been theobjects for the other datasets.

Fig.6 Data augmentation methods used in this research (Flip, 90° Rotate, Mosaic). First step is to transfer the preprocess image toFlip and 90° Rotate ones (i.e., the green line), the second step is to re-combine the transferred ones in the mosaic way.

101

Intelligence, Informatics and Infrastructure, Volume 5, Issue 1, 2024

Table 1 Model-related parameter setting.

5. Training the YOLOv8 model using HRB-WD:
As the parameters shown in Table 1, the authors
trained the YOLOv8n-seg model for the on-sitepractical waste segmentation. Shown in the Fig.7, themodel has stopped at epochs 251 with a smoothgoing-down val/box_loss and val/seg_loss curves.
Fig.7 Results of the trained YOLOv8n-seg derived from HRBWD.

(5) Objects of this work
The work aims to validate the efficacy of the pretrained models (YOLOv8n-seg, YOLOv8x-seg andSAM) and custom-trained YOLOv8n-seg modelthrough on-site testing using smartphone videos fromdistinct field sections, providing real-world
validation on the waste pollution after long-termsunshining and water brush-up.
In this research, the authors did not separate thewaste pollution into different species, just considered
as one class. The reason of using just one label isbased on the randomness of the on-site waste
pollution species, and the similar color of the objects.
In another word, the authors are seeking forsomething that are not made by natural environment
with the specified shapes (i.e., bottle shape).
Additionally, the research seeks to contribute toenvironmental protection initiatives and emphasize
the imperative for sustainable solutions. The
generated dataset is envisioned as a valuable resource
supporting both volunteers and government staff in
waste amount analysis, thereby facilitating informed
decision-making in the future, especially in the caseof analyzing the amount of the workload for
calculating the volunteers’ numbers estimation. In
this research, the authors prefer to upload the dataset
as the benchmark for the practical application.

The results showcased in Fig.8 (a) that the trainedmodel has detected 24 waste instances. And all thewaste instances have been detected over 0.6
confidence. When the waste pollution overlappedwith each other, especially the colors of the objects
are close, the instance segmentations among theobjects were difficult.
(2) Result derived from YOLOv8n/x-seg (pretrained)
The results in Fig.8 (b) performed that thepretrained model has not successfully detected any
bottle instance. And in the Fig.8 (c), 16 bottleinstances (total approximate 40 bottle instances inthis image, detection ratio is almost 40%) have been
detected over 0.6 confidence. Comparing with theResult-1, even the YOLOv8x-seg (pre-trained) has
more parameters and trained with the COCO dataset,
the YOLOv8n-seg (HRB-WD) performed muchbetter.
(3) Result derived from SAM
Fig.8 (d) has showed the segmentation of all thefactors in the image, and almost all the waste
pollutions have been separated from the backgroundas individual instances or partly. Based on this result,
YOLOv8 model also has the potential of improving
the accuracy in extracting the waste pollution fromthe background to a much better level with more data.

3. RESULTS AND DISCUSSION(1) Result derived from YOLOv8n-seg (HRBWD)

102

Intelligence, Informatics and Infrastructure, Volume 5, Issue 1, 2024

to the background and natural environment).
This study also emphasizes the potential of thegenerated dataset as a valuable tool for waste amountanalysis, supporting both volunteers and government
staff in environmental protection initiatives9).
Looking ahead, this research aspires to contribute to
informed decision-making and sustainable solutionsin the ongoing battle against waste pollution.

4. CONCLUSION
This study presented a novel dataset for waste
pollution segmentation in riparian area using asmartphone equipped with a 48MP digital camera fordata collection, the YOLOv8n model for data
analysis, and collected dataset was uploaded for thebenchmark. There are also some limitations in waste
instances segmentation, and the dataset also need to
be improved in the future.
ACKNOWLEDGMENT: This research wassupported in part by the DOWA HOLDINGS Co.,
Ltd. and TOHO ELECTRIC INDUSTRIAL Co., Ltd.
REFERENCES
Fig.8 Comparison of waste-based segmentation from different
models.

1)
2)
3)
4)

5)
6)
7)

Fig.9 Comparison of PET-bottle-objects used in different dataset(4) Discussion
From the mentioned results, YOLOv8n-seg (HRBWD) has performed the advantages comparing withthe pre-trained YOLOv8n-seg, even the YOLOv8xseg (pre-trained) cannot also overcome the result
derived from YOLOv8n-seg (HRB-WD). Fig.8 (d)displayed the well-segmented result without labels.
In the future, the combination of YOLOv8 and SAM
are in consideration for the waste pollution detection.
Contrary to the instances settled in the otherbenchmark dataset4) (i.e., UAV-BD and 4cls RMD)
or AIGC8) as performed in the Fig.9, this researchunderscores the practical riparian waste pollutions
are in a totally different situation (i.e., similar color8)

9)Hong, J., Fulton, M., & Sattar, J. Trashcan: A semanticallysegmented dataset towards visual detection of marine debris,
arXiv preprint arXiv:2007.08097, 2020.
http://tacodataset.org/
https://github.com/garythung/trashnet
Pan, S., Yoshida, K., Boney, A. S., & Nishiyama, S. The
Application of Drone-Assisted Deep Learning Technology
in Riverbank Garbage Detection. Journal of Japan Societyof Civil Engineers, Ser. B1 (Hydraulic Engineering), 78(2),I_133-I_138, 2022.
https://roboflow.com/
https://github.com/ultralytics/ultralytics
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C.,
Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo,W.Y., Dollár, P. & Girshick, R. Segment anything. arXiv
preprint arXiv:2304.02643, 2023.
Shijun, P. A. N., YOSHIDA, K., & KOJIMA, T.
Application of the Prompt Engineering-assisted GenerativeAIfortheDrone-based
Riparian
Waste
Detection. Intelligence, Informatics and Infrastructure, 4(2),50-59, 2023.
Shovon, S. M., Akash, F. A., Rahman, M. A., Rahman, W.,
Chakraborty, P., Monir, M. U., Karim, K. M. R., Habib, M.
A. & Khan, M. F. H. The potential for sustainable waste
management and energy recovery in Bangladesh: Areview. Sustainable Energy Technologies andAssessments, 64, 103705, 2024.
(Received January 15, 2024)(Accepted March 11, 2024)

103