Accurate monitoring of activated sludge microorganisms is critical for maintaining the stable operation of wastewater treatment systems. However, due to their semi-transparent morphology and high similarity to the surrounding environment, these microorganisms exhibit camouflaged characteristics, rendering traditional detection methods ineffective. To address the camouflage characteristics of activated sludge microorganisms, the diversity of object scales, and the ambiguity of boundaries in complex contexts, this paper proposes a camouflaged object detection method based on multi-scale awareness and edge enhancement. The proposed method employs a multi-scale feature aware module to extract rich contextual information through parallel processing and progressive expansion of the receptive field, thereby enhancing multi-scale feature representation. An edge-aware enhancement module is introduced to fuse low-level edge details with high-level semantic information for more accurate edge feature extraction. These edge features are then integrated with the multi-scale features through an attention-guided feature module, enabling the network to focus on the positional information of edges. Finally, a context aggregation module is used to progressively aggregate multi-level features in a top-down manner, further refining the prediction and generating the final output. On the benchmark camouflaged object detection dataset and the self-constructed activated sludge microorganism camouflage dataset, the proposed method achieves improvements of 2.2%, 4.1%, and 2.1%, and 1.2%, 2.2%, and 0.6% in terms of the evaluation metrics S-measure, weighted F-measure, and E-measure, respectively. Experimental results demonstrate that the proposed method achieves superior performance over other models across all datasets.