Line-labelling enhanced CNNs for transparent juvenile fish crowd counting
Counting juvenile fish in aquaculture is challenging due to their small, fragile, and often transparent bodies, especially under high-density conditions. To address this, we propose a novel line-labeling annotation method specifically designed for transparent juvenile fish counting, which enhances s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525001960 |
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| Summary: | Counting juvenile fish in aquaculture is challenging due to their small, fragile, and often transparent bodies, especially under high-density conditions. To address this, we propose a novel line-labeling annotation method specifically designed for transparent juvenile fish counting, which enhances supervision quality and provides both positional and morphological cues. We also introduce an improved CSRNet-based convolutional neural network, optimized for high-density fish scenarios. A dataset of 9000 annotated images of Silver Carp and Tilapia, categorized into four density ranges (0–10, 10–20, 20–30 and 30–40 fish/cm²), was used to train and evaluate our method. To determine the optimal approach, four combinations of labeling and image enhancement methods were tested: Point Labeling + Original Image (P + O), Line Labeling + Original Image (L + O), Point Labeling + Image Enhancement (P + I) and Line Labeling + Image Enhancement (L + I). Counting accuracy was assessed using heatmap-based visualizations. Experimental results demonstrate that the line-labeling method significantly improves counting accuracy, achieving 97.73 % for Silver Carp and 98.04 % for Tilapia, outperforming conventional point-based annotations in high-density contexts. This study highlights the potential of structured annotations and tailored network designs for advancing precision in fish counting tasks. |
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| ISSN: | 2772-3755 |