Objectness Region Enhancement Networks for Scene Parsing

Semantic segmentation has recently witnessed rapid progress, but existing methods only focus on identifying objects or instances. In this work, we aim to address the task of semantic understanding of scenes with deep learning. Different from many existing methods, our method focuses on putting forward some techniques to improve the existing algorithms, rather than to propose a whole new framework. Objectness enhancement is the first effective technique. It exploits the detection module to produce object region proposals with category probability, and these regions are used to weight the parsing feature map directly. 'Extra background' category, as a specific category, is often attached to the category space for improving parsing result in semantic and instance segmentation tasks. In scene parsing tasks, extra background category is still beneficial to improve the model in training. However, some pixels may be assigned into this nonexistent category in inference. Black-hole filling technique is proposed to avoid the incorrect classification. For verifying these two techniques, we integrate them into a parsing framework for generating parsing result. We call this unified framework as Objectness Enhancement Network (OENet). Compared with previous work, our proposed OENet system effectively improves the performance over the original model on SceneParse150 scene parsing dataset, reaching 38.4 mIoU (mean intersection-over-union) and 77.9% accuracy in the validation set without assembling multiple models. Its effectiveness is also verified on the Cityscapes dataset.

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is introduced.It minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available vehicles.According to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,respectively.Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies.Furthermore,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP.It provides a promising scalability to the problem size.

Effects of Different Initial Fields on GRAPES Numerical Prediction

In this paper, a heavy rainfall process occurring in the Huaihe River Basin during 9-10 July 2005 is studied by the new generation numerical weather prediction model system-GRAPES, from the view of different initial field effects on the prediction of the model. Several numerical experiments are conducted with the initial conditions and lateral boundary fields provided by T213 L31 and NCEP final analyses, respectively. The sensitivity of prediction products generated by GRAPES to different initial conditions, including effects of three-dimensional variational assimilation on the results, is discussed. After analyzing the differences between the two initial fields and the four simulated results, the memonic ability of the model to initial fields and their influences on precipitation forecast are investigated. Analyses show the obvious differences of sub-synoptic scale between T213 and NCEP initial fields, which result in the corresponding different simulation results, and the differences do not disappear with the integration running. It also shows that for the same initial field whether it has data assimilation or not, it only obviously influences the GRAPES model results in the initial 24 h. Then the differences reduce. In addition, both the location and intensity of heavy rain forecasted by GRAPES model Further is very close to the fact, but the forecasting area of strong torrential rain has some differences from the fact. For the same initial field when it has assimilation, the 9-12-, 12-24-, and 0-24-h precipitation forecasts of the model are better than those without assimilation. All these suggest that the ability of GRAPES numerical prediction depends on the different initial fields and lateral boundary conditions to some extent, and the differences of initial fields will determine the differences of GRAPES simulated results.