Work

EPAF-DETR:Efficient transformer-based model for abnormal fish behavior detection under water quality anomalies

Year: 2025

Type: article

Abstract: In aquaculture management, the prompt recognition of abnormal fish behaviors induced by external stimuli or diseases is crucial for enhancing breeding efficiency and securing economic returns for farmers. Nevertheless, monitoring such behaviors remains challenging owing to intricate aquatic environments, frequent occlusions, and considerable visual similarities across different behavioral categories. To tackle these issues, this study introduces an Efficient Parallel Attention Fusion Detection Transformer, designated as EPAF-DETR, which is developed to achieve high-precision and robust object detection. By integrating EfficientViT as the backbone network, the computational complexity of the model is significantly reduced. Combined with an adaptive sparse self-attention mechanism and a spatially enhanced feedforward network, an improved AIFI module is introduced to strengthen feature extraction capabilities. Furthermore, Multi-Level Hierarchical Attention Fusion module is designed to enhance the original cross-scale feature fusion component in RT-DETR, enhancing the salience of critical features and further improving detection accuracy. Finally, by incorporating Matchability-Aware Loss function, the model is guided to place greater emphasis on matching low-quality features.These architectural advancements considerably boost the model’s adaptability in demanding underwater settings and augment its capacity to discriminate fine-grained behavioral characteristics of fish. Experimental outcomes indicate that EPAF-DETR attains detection performance while reducing computational costs, achieving an average F1-score of 94 % and a mAP of 95.7 %. In conclusion, the proposed approach effectively addresses detection difficulties in complex aquaculture environments, enabling accurate and reliable identification of anomalous fish behaviors.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101681

Authors: Xun Chen, Chuang Wang, Siyu Wu, Xinjian Ou

Institutions: Hainan University, State Key Laboratory of Marine Resource Utilization in South China Sea

Language: English

Topic: Water Quality Monitoring Technologies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Life below water

Open Access status: gold

Estimated APC paid: $1,770