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

• Published in: Ecological indicators Vol. 167; p. 112597
• Main Authors: Wang, Daoli, Dong, Zengchuan, Yang, Guang, Li, Weiwei, Wang, Yingying, Wang, Wenzhuo, Zhang, Yang, Lü, Zhonghai, Qin, Youwei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024; Elsevier
• Subjects: APNet-YOLOv8s; Aquatic plants; Computer vision; Deep learning; Target detection; Deep learning; Computer vision; APNet-YOLOv8s; Target detection; Aquatic plants
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2024.112597

[Display omitted] •A novel method (APNet-YOLOv8s) for real-time detection of aquatic plants in complex environments is presented.•A new aquatic plant image dataset for complex environments was constructed, including 12 plants of four life forms.•GRF-SPPF, SA and FD structures were integrated into the YOLOv8s model to address the specific challenges posed by aquatic plants.•APNet-YOLOv8s shows superior performance over the YOLOv8s model in different test environments. 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.