OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the CODA dataset show that the proposed method can obtain better performance than that of the existing methods

When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best fits the data property. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among subpopulations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA

Research Progress in Heterologous Expression, Fermentation and Application of Microbial Transglutaminase

Transglutaminase (TG) is a widely used enzyme with excellent protein cross-linking capacity. TG is commonly found in plants, animals and microorganisms, and microbial TG (mTG) is widely used in industrial production and application because of its good enzymatic properties. This paper describes the physicochemical properties and activation mechanism of mTG, and summarizes recent progress in mTG production by wild and different genetically engineered strains. Meanwhile, the application and potential of mTG in various industrial fields are reviewed. This review is expected to provide a reference and new ideas for research on the potential of mTG for industrial production and application

Effect of perforation shear on viscosity of polymer solution

Polymer flooding is a tertiary oil recovery technology that is very suitable for the characteristics of China’s reservoirs. However, due to the fast flow rate of polymer solution in near-well zone, the shear effect of perforation blasthole and the compacted zone results in serious loss of polymer viscosity. In this paper, the polymer used in Dagang Oilfield is studied by simulation experiment through the shearing process of perforating holes, and the influence of different perforating parameters on polymer viscosity loss is analyzed, so as to provide theoretical basis for the optimization design of perforating technology in field test. The experimental results show that, the shear effect of perforation blasthole on polymer is not obvious, and the viscosity retention rate of polymer solution is greater than 96%. The size and shape of perforation blasthole have no effect on viscosity loss of polymer solution. The shear effect of compacted zone on polymer is obvious, and the viscosity retention rate of polymer solution is lower than 64% for the target block. The viscosity loss of polymer solution increases with flow rate at compacted zone, and the decrease of permeability can increase viscosity loss of polymer solution. The higher the polymer concentration is, the stronger the shear resistance is, while the higher the molecular weight is, the weaker the shear resistance is. It is suggested that perforation gun and perforation method with deep perforation depth and low compaction degree be chosen to reduce the flow rate at compacted zone and viscosity loss of polymer solution