Adaptive Gating‐Enhanced Multi‐Feature Fusion for Zooplankton Image Classification

ABSTRACT Zooplankton image classification is important for marine biodiversity monitoring and environmental research. Zooplankton image classification faces several challenges, including complex background noise, intraspecific phenotypic variation, interspecific morphological similarity and limited original data and insufficient training sets. To overcome these limitations, we propose an adaptive gate‐enhanced multi‐feature hybrid fusion‐ShuffleNetV2(AHFNet) algorithm using a transfer learning framework. ShuffleNetV2 is a lightweight convolutional neural network that has been pre‐trained on various large‐scale datasets. Therefore, we adopt the pre‐trained parameters of ShuffleNetV2 from the ImageNet dataset for fine‐tuning, adapting it to zooplankton image classification. We combine three feature channels and concatenate them to form multi‐source inputs to enhance feature complementarity. The three channels include the original feature image, global shape features obtained by Gaussian low‐pass filtering, and local texture features obtained by Gabo filtering. We designed a hybrid fusion module with a conditional gating mechanism that dynamically fuses channel attention (via the Squeeze and excitation module) and spatial attention (via the Parallelized patch‐aware mechanism). A pivotal aspect of our design is the phased network layout: we deploy a standalone SE module in Stage2 to suppress background noise, while the hybrid SE‐PPA module in Stages3 and 4 performs joint channel recalibration and multi‐scale spatial feature capture. This is the first work to adapt and synergize the PPA mechanism with SE for this specific task through optimized stage‐wise integration. Experimental results demonstrate the effectiveness of our method, which achieves accuracy on the Kaggle38 dataset and on the WHOI25 dataset, with ablation studies conclusively validating our design choices.