Microscopic observation of indicator microorganisms in activated sludge is an important mean to evaluate the operating status of the sewage treatment process. Microbial communities in the context of activated sludge have a high degree of camouflage characteristics, making it difficult for traditional target detection methods to accurately identify them. To address the challenges of detecting camouflaged microbial targets, this paper constructs a camouflaged activated sludge microorganisms (CASM) dataset, which contains 25 species of activated sludge microorganisms and a total of 1888 images. This paper proposes a novel context-aware camouflage object detection model for microorganisms in activated sludge, referred to as the context-aware search identification network (CA-SINet). The proposed model employs PVT v2 as the backbone network to extract camouflage features and designs a camouflage context sensor to integrate local and global information through multi-scale feature fusion. By fully leveraging contextual information from the surrounding environment, CA-SINet achieves fine-grained detection of camouflaged microorganism features. Experiments are conducted on four public datasets as well as a self-constructed CASM dataset. Experimental results on three public datasets where the optimal results are obtained indicate that, compared with the suboptimal model, S value, weighted F value, and E value of proposed model increase by an average of 0.027, 0.008, and 0.015, respectively, and the average absolute error decreases by an average of 0.008. Experimental results on self-constructed CASM dataset indicate that, compared with the suboptimal model, S value, weighted F value, and E value of proposed model increase by 0.001, 0.010, and 0.006, respectively, and the average absolute error decreases by 0.001. CA-SINet exhibits significant performance advantages in camouflage object detection tasks.