Research on Improved Near-Infrared Fish Density Classification Method Based on ResNet18

Addressing the technological requirement for real-time monitoring of fish density in dim aquaculture environments, this study proposes a near-infrared (NIR) image classification method using a modified ResNet18 architecture. Initially, an NIR-Fish dataset comprising 736 high-quality annotated images (256 × 256 resolution) spanning three density scenarios (low, medium, and high density) was constructed. Contrast-Limited Adaptive Histogram Equalization (CLAHE) preprocessing was implemented with an 8 × 8 tiling strategy and clip limit = 4.0, significantly enhancing the discernibility of faint boundary features. A dual-channel attention module (DCAM) was embedded into the ResNet18 backbone, featuring a parallel architecture integrating Global Average Pooling (GAP) and Global Max Pooling (GMP). This design synergistically optimized local salient feature enhancement and global statistical feature fusion through parameter-shared fully connected layers (reduction ratio of 16:1). The experiments show that the classification accuracy of the proposed method on the independent test set is 80.57%, which is 4.34 percentage points higher than that of the original ResNet18. F1 scores for the three density levels were 0.8308 (low), 0.7674 (medium), and 0.8294 (high), respectively. Ablation studies confirmed the dual-channel design’s significant performance contribution, while the parameter-sharing mechanism effectively mitigated overfitting risks. By leveraging feature complementarity and lightweight design, this work overcomes the classification bottleneck for NIR images under low signal-to-noise conditions, providing a highly robust technical solution for intelligent aquaculture management.