| Autor: |
Ximing Li, Yitao Zhuang, Baihao You, Zhe Wang, Jiangsan Zhao, Yuefang Gao, Deqin Xiao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 7, Pp 102143- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1319-1578 |
| DOI: |
10.1016/j.jksuci.2024.102143 |
| Popis: |
Fish counting is crucial in fish farming. Density map-based fish counting methods hold promise for fish counting in high-density scenarios; however, they suffer from ineffective ground truth density map generation. High labeling complexities and disturbance to fish growth during data collection are also challenging to mitigate. To address these issues, LDNet, a versatile network with attention implemented is introduced in this study. An imbalanced Optimal Transport (OT)-based loss function was used to effectively supervise density map generation. Additionally, an Image Manipulation-Based Data Augmentation (IMBDA) strategy was applied to simulate training data from diverse scenarios in fixed viewpoints in order to build a model that is robust to different environmental changes. Leveraging a limited number of training samples, our approach achieved notable performances with an 8.27 MAE, 9.97 RMSE, and 99.01% Accuracy on our self-curated Fish Count-824 dataset. Impressively, our method also demonstrated superior counting performances on both vehicle count datasets CARPK and PURPK+, and Penaeus_1k Penaeus Larvae dataset when only 5%–10% of the training data was used. These outcomes compellingly showcased our proposed approach with a wide applicability potential across various cases. This innovative approach can potentially contribute to aquaculture management and ecological preservation through counting fish accurately. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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