Detection of algal tiny objects based on morphological features

Summary The dense and toxic blooms formed by cyanobacteria in aquatic environments pose significant threats to public health and aquatic ecosystems. Timely monitoring and prevention of cyanobacterial blooms in freshwater bodies are thus imperative. Although object detection methods have been applied in the field of algae identification, existing research faces several challenges. A primary issue is the overly idealistic setting of training sets, which are disconnected from the actual water quality environments, impeding practical algae identification and water quality monitoring. In this paper, we collect 2024 microscopic images of algae from a reservoir in Southern China, forming a comprehensive and diverse dataset for object detection. Addressing the aforementioned challenges, we propose an attention‐based strategy for the detection of tiny algal objects, which effectively samples and extracts features from algal targets. Our model uniquely leverages the morphable characteristics of algae, enhancing the accuracy and efficiency of identification. The training and improvement results of this model presented in our study are expected to aid in establishing a future system for real‐time algae monitoring and water quality assessment.