Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration

• URL: http://arxiv.org/abs/2501.04873v1
• Date: Wed, 08 Jan 2025 23:07:10 GMT
• Title: Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration
• Authors: Alexander Valverde, Luis Solano,
• Abstract summary: In Costa Rica, an average of 5 tons of seashells are extracted from ecosystems annually. Confiscated seashells, cannot be returned to their ecosystems due to the lack of origin recognition. We developed a convolutional neural network (CNN) specifically for seashell identification. We built a dataset from scratch, consisting of approximately 19000 images from the Pacific and Caribbean coasts. The model has been integrated into a user-friendly application, which has classified over 36,000 seashells to date, delivering real-time results within 3 seconds per image.
• Score: 49.1574468325115
• License:
• Abstract: In Costa Rica, an average of 5 tons of seashells are extracted from ecosystems annually. Confiscated seashells, cannot be returned to their ecosystems due to the lack of origin recognition. To address this issue, we developed a convolutional neural network (CNN) specifically for seashell identification. We built a dataset from scratch, consisting of approximately 19000 images from the Pacific and Caribbean coasts. Using this dataset, the model achieved a classification accuracy exceeding 85%. The model has been integrated into a user-friendly application, which has classified over 36,000 seashells to date, delivering real-time results within 3 seconds per image. To further enhance the system's accuracy, an anomaly detection mechanism was incorporated to filter out irrelevant or anomalous inputs, ensuring only valid seashell images are processed.

Related papers

• BenthicNet: A global compilation of seafloor images for deep learning applications [25.466405216505166]
  BenthicNet is a global compilation of seafloor imagery. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks.
  arXiv  Detail & Related papers  (2024-05-08T17:37:57Z)
• WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database [49.1574468325115]
  We introduce textbfWhaleNet (Wavelet Highly Adaptive Learning Ensemble Network), a sophisticated deep ensemble architecture for the classification of marine mammal vocalizations. We achieve an improvement in classification accuracy by $8-10%$ over existing architectures, corresponding to a classification accuracy of $97.61%$.
  arXiv  Detail & Related papers  (2024-02-20T11:36:23Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra [3.254879465902239]
  This study utilizes a specialized dataset consisting of 923 images collected from Flickr using the Flickr API. The method employed in this research involves the use of machine learning models, particularly convolutional neural networks (CNN) It was found that a from-scratch ResNet model can outperform pretrained models in terms of precision and accuracy.
  arXiv  Detail & Related papers  (2023-08-08T15:30:08Z)
• Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change [6.335630432207172]
  Corals are the primary habitat-building life-form on reefs that support a quarter of the species in the ocean. For the first time, 3D fine-grained semantic modeling and rugosity evaluation of coral reefs have been completed at millimeter (mm) accuracy.
  arXiv  Detail & Related papers  (2022-12-08T08:09:57Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• Instance Segmentation of Microscopic Foraminifera [0.0629976670819788]
  We present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset.
  arXiv  Detail & Related papers  (2021-05-15T10:46:22Z)
• Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction [57.74609918453932]
  Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. We propose a hybrid representation learning approach to address this challenge.
  arXiv  Detail & Related papers  (2020-12-14T05:22:49Z)
• Deep learning for lithological classification of carbonate rock micro-CT images [52.77024349608834]
  This work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images. Four convolutional neural network models were proposed. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second.
  arXiv  Detail & Related papers  (2020-07-30T19:14:00Z)
• Temperate Fish Detection and Classification: a Deep Learning based Approach [6.282069822653608]
  We propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering.
  arXiv  Detail & Related papers  (2020-05-14T12:40:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.