Anomaly detection algorithms typically utilize generative models to reconstruct anomaly regions. Post-processing is used to pinpoint the anomalies. However, the paucity of real-world anomaly samples and the complex image backgrounds pose significant challenges for anomaly detection. The work innovatively proposed a self-supervised anomaly detection method. An efficient channel attention mechanism in the autoencoder was introduced to improve the reconstruction performance. Besides, a foreground enhancement strategy was designed to distinguish the foreground from the background by maximizing the inter-class variance. The strategy reduced the effect of background noises and simulated various anomalies that were rare in real samples. The MVTecAD and BTAD datasets were used to experiment with anomaly detection and location. Experimental results demonstrated that our method achieved higher AUC and AP scores at both the image level and pixel level compared to other advanced methods. In particular, the average AP score increased by 12.5% at the pixel level.

Accurate microscopic images segmentation of activated sludge is essential for monitoring wastewater treatment processes. However, it is a challenging task due to poor contrast, artifacts, morphological similarities, and distribution imbalance. A novel image segmentation model (FafFormer) was developed in the work based on Transformer that incorporated pyramid pooling and flow alignment fusion. Pyramid Pooling Module was used to extract multi-scale features of flocs and filamentous bacteria with different morphology in the encoder. Multi-scale features were fused by flow alignment fusion module in the decoder. The module used generated semantic flow as auxiliary information to restore boundary details and facilitate fine-grained upsampling. The Focal–Lovász Loss was designed to handle class imbalance for filamentous bacteria and flocs. Image-segmentation experiments were conducted on an activated sludge dataset from a municipal wastewater treatment plant. FafFormer showed relative superiority in accuracy and reliability, especially for filamentous bacteria compared to existing models.