Proceedings of The 6th International Workshop on Deep Learning in Computational Physics

Underwater biotope mapping: automatic processing of underwater video data 水下生物群落制图:水下视频数据的自动处理

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0024

O. Iakushkin, Ekaterina Pavlova, Anastasia Lavrova, Eugene Pen, O. Sedova, Vyacheslav Polovkov, N. Shabalin, Terekhina Yana, Frih-Har Anna

{"title":"Underwater biotope mapping: automatic processing of underwater video data","authors":"O. Iakushkin, Ekaterina Pavlova, Anastasia Lavrova, Eugene Pen, O. Sedova, Vyacheslav Polovkov, N. Shabalin, Terekhina Yana, Frih-Har Anna","doi":"10.22323/1.429.0024","DOIUrl":"https://doi.org/10.22323/1.429.0024","url":null,"abstract":"The task of analysing the inhabitants of the underwater world applies to a wide range of applied problems: construction, fishing, and mining. Currently, this task is applied on an industrial scale by a rigorous review done by human experts in underwater life. In this work, we present a tool that we have created that allows us to significantly reduce the time spent by a person on video analysis. Our technology offsets the painstaking video review task to AI, creating a shortcut that allows experts only to verify the accuracy of the results. To achieve this, we have developed an observation pipeline by dividing the video into frames; assessing their degree of noise and blurriness; performing corrections via resolution increase; analysing the number of animals on each frame; building a report on the content of the video, and displaying the obtained data of the biotope on the map. This dramatically reduces the time spent analysing underwater video data. Also, we considered the task of biotope mass calculation. We correlated the Few-shot learning segmentation model results with point cloud data to achieve that. That provided us with a biotope surface coverage area that allowed us to approximate its volume. Such estimation is helpful for precise area mapping and surveillance. Thus, this paper presents a system that allows detailed underwater biotope mapping using automatic processing of a single camera underwater video data. To achieve this, we combine into a single pipeline a set of deep neural networks that work in tandem.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"130 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122847194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hazy images dataset with localized light sources for experimental evaluation of dehazing methods 基于局部光源的雾霾图像数据集，用于除雾方法的实验评估

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0019

A. Filin, A. Kopylov, O. Seredin, I. Gracheva

{"title":"Hazy images dataset with localized light sources for experimental evaluation of dehazing methods","authors":"A. Filin, A. Kopylov, O. Seredin, I. Gracheva","doi":"10.22323/1.429.0019","DOIUrl":"https://doi.org/10.22323/1.429.0019","url":null,"abstract":"Image haze removal methods have taken increasing attention of researchers. At the same time, an objective comparison of haze removal methods struggles because of the lack of real data. Capturing pairs of images of the same scene with presence/absence of haze in real environment is a very complicated task. Therefore, the most of modern image haze removal datasets contain artificial images, generated by some model of atmospheric scattering and known scene depth. Among the few real datasets, there are almost no datasets consisting of images obtained in low light conditions with artificial light sources, which allows evaluating the effectiveness of nighttime haze removal methods. In this paper, we present such dataset, consisting of images of 2 scenes at 4 lighting levels and 4 levels of haze density. The scenes has varying \"complexity\" – the first scene consists of objects with a simpler texture and shape (smooth, rectangular and round objects); the second scene is more complex – it consists of objects with small details, protruding parts and localized light sources. All images were taken indoors in a controlled environment. An experimental evaluation of state-of-the-art haze removal methods was carried out on the collected dataset.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124029585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A machine learning approach to identify the air shower cores for the GRAPES-3 experiment 一种机器学习方法来识别GRAPES-3实验的风淋室核心

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0001

M. Chakraborty, S. Ahmad, A. Chandra, S. Dugad, U. D. Goswami, S. Gupta, B. Hariharan, Y. Hayashi, P. Jagadeesan, Akshay Jain, P. Jain, S. Kawakami, H. Kojima, S. Mahapatra, P. Mohanty, R. Moharana, Y. Muraki, P. K. Nayak, T. Nonaka, A. Oshima, S. Paul, B. Pant, D. Pattanaik, G. Pradhan, M. Rameez, K. Ramesh, L. V. Reddy, R. Sahoo, R. Scaria, S. Shibata, K. Tanaka, F. Varsi, M. Zuberi

引用次数: 0

Deep neural network applications for particle tracking at the BM@N and SPD experiments 深度神经网络在BM@N和SPD实验中的粒子跟踪应用

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0005

P. Goncharov, D. Rusov, Anastasiia Nikolskaia, E. Shchavelev, G. Ososkov

引用次数: 1

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)最新文献