CarrDet: Attention–Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment

Abstract

The carrier is one of the key components used in wastewater treatment, which can enrich microorganisms at the surface to improve the amount of biomass in the reactor. Monitoring and adjusting the number of carriers is a key component for the processing efficiency of the ecosystem, which directly impacts wastewater treatment effectiveness. Therefore, carrier detection in wastewater microscopic images is important in the urban domestic wastewater treatment process. The current process to detect carriers is operator-dependent, which is time-consuming and expensive. Though a number of general object or cell detection approaches are used for this task, their effectiveness is limited because the carrier and background are similar and there are defective carriers as background noise. In this paper, we propose CarrDet, the first deep learning-based carrier detection framework for wastewater treatment. CarrDet uses a carrier feature block attention module and a symmetry-based defective carrier detection module to detect carriers with shallow edges and reduce false positives caused by defective carriers, respectively. To evaluate CarrDet, we propose a carrier dataset of 600 wastewater microscopic images, manually annotated by experts. Compared with state-of-the-art object detection methods, CarrDet shows superior performance in terms of both accuracy and speed, achieving a mAP of 94.32 and an IPS of 4.93. We employed CarrDet to confirm the detection results of 621 wastewater microscopic images, which were detected by inexperienced engineers who are new to the field. CarrDet added 398 unrecognized carriers with shallow edges and corrected 273 incorrect manual annotations in 5 min, which emphasizes the efficiency and practicality of CarrDet for practical business scenarios.

Keywords

• carrier detection
• microscopic images
• wastewater treatment
• attention mechanism
• deep learning