the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Automatic detection of instream large wood in videos using deep learning
Abstract. Instream large wood (i.e., downed trees, branches and roots larger than 1 m in length and 10 cm diameter) has essential geopmorphological and ecological functions supporting the health of river ecosystems. Still, even though its transport during floods may pose a risk, it is rarely observed and, therefore, poorly understood. This paper presents a novel approach to detect pieces of instream wood from video. The approach uses a Convolutional Neural Network to detect wood automatically. We sampled data to represent different wood transport conditions, combining 20 datasets to yield thousands of instream wood images. We designed multiple scenarios using different data subsets with and without data augmentation and analyzed the contribution of each one to the effectiveness of the model using k-fold cross-validation. The mean average precision of the model varies between 35 and 93 percent, and is highly influenced by the quality of the data which it detects. When the image resolution is low, the identified components in the labeled pieces, rather than exhibiting distinct characteristics such as bark or branches, appear more akin to amorphous masses or 'blobs'. We found that the model detects wood with a mean average precision of 67 percent when using a 418 pixels input image resolution. Also, improvements of up to 23 percent could be achieved in some instances and increasing the input resolution raised the weighted mean average precision to 74 percent. We show that the detection performance on a specific dataset is not solely determined by the complexity of the network or the training data. Therefore, the findings of this paper can be used when designing a custom wood detection network. With the growing availability of flood-related videos featuring wood uploaded to the internet, this methodology facilitates the quantification of wood transport across a wide variety of data sources.
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Status: open (until 06 Jul 2024)
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CC1: 'Comment on egusphere-2024-792', Andrés Iroumé, 14 May 2024
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Is a very well written and interesting manuscript.
I have a few suggestions intended to complete/improve some aspects.
They are:
Introduction:
- Page 1, L19-20. Natural mortality wind, snow loads, wildfires and beaver activities can also be recruitment sources.
- Page 1, L20. “Wood plays a crucial role by trapping sediment, creating pools, and generating spatially varying flow patterns” not only as it distributes along the riverbanks, but also when stored withing the active or bankfull channel.
- Page 2, L34. The number of observations of instream wood is scarce? I do not fully agree. Perhaps the amount of observations of instream wood dynamics is scarce, so please clarify.
- Page 2, L43, about the best methods to quantify wood transport. Not only video-based methods, but also the installation of a GPS in each wood is a very good method, but extremely expensive.
Methods:
- Page 3, L86. Figure 1does not give an overview of the data collection and processing. It gives an overview of the process to follow to collect and process data. Please also correct the title of Fig. 1 below the figure.
- Page 4, L107 and 115. Figure or figure? Please decide.
Discussion and conclusion:
- I do not find comments related to the limitations of the use of low-cost cameras, and how to avoid these limitations, may be by using high resolution cameras, installations, others. Please discuss and conclude.
Citation: https://doi.org/10.5194/egusphere-2024-792-CC1 -
RC1: 'Comment on egusphere-2024-792', Diego Panici, 14 Jun 2024
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The manuscript is about the automatic detection of instream large wood in video recording using deep learning tools. The results are really intriguing, but I believe that a substantial revision will be needed before considering this paper for publication. Here are some major comments:
First, there is limited to no comparison with other existing models. CNNs are widely used for image recognition (and, indeed, the quthors acknowledged YOLO being the most wide spread algorithm), yet, there is no comparative analysis with other studies or algorithms.
Second, the overall aim and output of this manuscript is really unclear. It is necessary to explicitate this further and emphasise what the study has revealed and what increase in scientific knowledge it has brought. As things stand, it is hard to discern what is the new scientific knowledge that this paper has produced.
Third, the paper structure needs substantial changes. The results and discussion sections merged together makes difficult to discern between the actual observations and the authors' analysis. It is essential that the two sections are kept separate. The language used is also not appropriate for a scientific paper: this was mostly informal and colloquial and needs thorough revision.
Fourth, the method was unclear and lacked explanation (at times it was not even easy to understand what cameras have been used, where and how, whilst a schematic would have helped). Overall, this limits the generalisation of the method proposed.
An annotated version is also provided with in-line comments.
Model code and software
Codebase for "Automatic Detection of Instream Large Wood in Videos Using Deep Learning" J. Aarnink and T. Beucler https://github.com/janbertoo/Instream_Wood_Detection
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