# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 1 Introduction  
A carrier is typically an attachable growth structure that can enrich microorganisms, such as cells and bacteria, at the surface to increase the amount of biomass in a bioreactor [1]. Adjusting and controlling the number of carriers can maintain an appropriate and effective microbial living environment [2]. The microbial system plays a vital role in wastewater treatment (WWT), as it degrades, transforms, and eliminates organic pollutants from wastewater. The accurate detection and quantification of carriers in wastewater maximizes the processing efficiency of the microbial system [3] in WWT and ensures that the effluent water quality complies with the set standards [2]. Carrier detection in microscopic images is a significant step in urban domestic WWT [4]. Manual detection methods are known to be time-consuming and subjective [5]. As a result, there has been considerable interest in deep learning-based approaches for carrier detection in wastewater microscopic images. Though there is no method proposed for carrier detection specifically, this task can be approached by general object detection and cell detection methods. General objectdetection methods, e.g., YOLO [6], SSD [7], and RCNN [8], can be fine-tuned for carrier detection, using the knowledge gained from general object detection tasks. Cell detection methods [9; 10] have refined the baseline, such as ResNet [11], with domain-specific knowledge from cell microscopic images. However, because of the morphological features of carrier microscopic images, the performance of these methods is limited when they are applied to detect carriers in wastewater microscopic images directly. Carriers closely resemble the background in color, which leads to unrecognized carriers with shallow edges (Figure 1a). Moreover, there is a significant presence of defective carriers acting as background noise, which leads to false positives caused by defective carriers (Figure 1b). Inaccurate carrier detection results can lead to incorrect assessments of carrier quantity by operators, which in turn causes erroneous judgments about the current wastewater conditions. This subsequently leads to inappropriate adjustments in operational procedures, ultimately affecting the effectiveness of water purification. As a result, there is an urgent need for a dedicated approach that can effectively address these challenges in order to enhance the accuracy of carrier detection in microscopic images. This will assist operators in making correct adjustments to operations, ultimately leading to the improvement of the WWT process. In order to tackle these challenges, we propose Attention-Symmetry-based Microscopic Carrier Detection (CarrDet), the first carrier detector of wastewater microscopic images for WWT. CarrDet mainly contains two modules: a carrier feature block attention module (CFBA) and a symmetry-based defective carrier detection module (SDCD). Specifically, we first employ the feature extractor to extract the carrier feature from the microscopic images. Subsequently, CFBA enhances the global perception of the feature through the integration of channel attention and spatial attention mechanisms. This module enlarges the contrast between the background and carrier edges and emphasizes edge features. Consequently, it effectively distinguishes unrecognized carriers with shallow edges from the background, thereby facilitating detection of them. Lastly, SDCD capitalizes on the symmetry of circular carriers to optimize the candidate frames produced by the detection head. By flipping the detection results and evaluating the pairwise similarity of corresponding positions, this approach checks if the results are symmetrical and circular. Thus, this approach reduces false positives caused by defective carriers, effectively filtering out background noise present in the images. The main contributions of this paper are summarized as follows:  
* We are the first to propose a carrier detection method specifically for wastewater microscopic images.