APNet-YOLOv8s: A real-time automatic aquatic plants recognition algorithm for complex environments

Abstract

Deep learning techniques have been widely utilized for image recognition tasks. However, these techniques remain challenging in detecting aquatic plants due to their complex growing environments, long phenological periods, high species similarity, and the fact that they are often obscured by surrounding objects. To overcome these challenges, this study presents a comprehensive dataset of aquatic plant images in complex environments (DS-AP) and proposes a novel method, APNet-YOLOv8s. APNet-YOLOv8s integrates three modules: the Global Receptive Field-Space Pooling Pyramid-Fast (GRF-SPPF), the Shuffle Attention (SA) Mechanism, and the Fast Detection (FD), each designed to tackle specific challenges in aquatic plant detection. The performance of APNet-YOLOv8s was thoroughly evaluated using the DS-AP dataset. The results demonstrate that APNet-YOLOv8s significantly outperforms YOLOv8s, achieving a mean average precision (mAP50) of 75.3 % with a 2.7 % improvement, and a frame per second (FPS) rate of 30.5 with a 50.2 % increase. Moreover, APNet-YOLOv8s accurately and rapidly identifies aquatic plants in Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations and real-world scenarios, highlighting its practical applications in complex environments. Overall, this study advances the application of deep learning in aquatic environments, providing a potential solution for rapid detection in other challenging environments.