Msb r‐cnn: A multi‐stage balanced defect detection network

Deep learning networks are applied for defect detection, among which Cascade R‐CNN is a multi‐stage object detection network and is state of the art in terms of accuracy and efficiency. However, it is still a challenge for Cascade R‐CNN to deal with complex and diverse defects, as the widely varied shapes of defects lead to inefficiency for the traditional convolution filter to extract features. Additionally, the imbalance in features, losses and samples cause lower accuracy. To address the above challenges, this paper proposes a multi‐stage balanced R‐CNN (MSB R‐CNN) for defect detection based on Cascade R‐CNN. Firstly, deformable convolution is adopted in different stages of the backbone network to improve its adaptability to the varying shapes of the defect. Then, the features obtained by the backbone network are refined and enhanced by the balanced feature pyramid. To overcome the imbalance of classification and regression loss, the balanced L1 loss is applied at different stages to correct it. Finally, for the sample selection, the interaction of union (IoU) balanced sampler and the online hard example mining (OHEM) sampler are combined at different stages to make the sampling more reasonable, which can bring a better accuracy and convergence effect to the model. The results of our experiments on the DAGM2007 dataset has shown that our network (MSB R‐CNN) can achieve a mean average precision (mAP) of 67.5%, an increase of 1.5% mAP, compared to Cascade R‐CNN

A missing link in the estuarine nitrogen cycle?: coupled nitrification-denitrification mediated by suspended particulate matter

In estuarine and coastal ecosystems, the majority of previous studies have considered coupled nitrification-denitrification (CND) processes to be exclusively sediment based, with little focus onsuspended particulate matter (SPM) in the water column. Here, we present evidence of CND processes in the water column of Hangzhou Bay, one of the largest macrotidal embayments in the world