Anchor-free instance segmentation and fast performance evaluation of Anammox granular sludge

Abstract

<p indent="0mm">The method of measuring the biochemical reaction equivalence of Anammox granular sludge is accurate but time-consuming, and the method based on image color modeling is fast but inaccurate enough. Therefore, the study proposes a new model combining the visual features of granular sludge (such as color, size, and roughness) for performance evaluation based on the instance segmentation task, which can guarantee high accuracy while performing a fast evaluation. A sampling scheme is first designed to collect the granular sludge images, and then image annotation is performed to obtain the granular sludge image dataset. This study proposes an improved BlendMask anchor-free instance segmentation method, which combines deformable convolution on the backbone network to extract small target information, adopts an adaptive sample selection strategy to balance samples on the head network, and redesigns the modules of postprocessing, loss function, and data augmentation to quickly and accurately extract the granular sludge individuals in image scenes. Then, the association model of visual features and performance is designed to obtain the visual features of granular sludge and discriminate the performance class of the input image. The proposed method can obtain a faster and more accurate evaluation of the performance of granular sludge. Finally, the effectiveness of the proposed method is verified. Compared with the previous methods, the improved BlendMask and the performance evaluation model have improved the accuracy of granular sludge image segmentation and performance evaluation by 3.93% and 1.49%, respectively, achieving the most advanced performance.