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Classification for Glass Bottles Based on Improved Selective Search Algorithm

Computers, Materials & Continua 2020年第7期64卷 233-251页

作者： Shuqiang Guo Baohai Yue manyang gao Xinxin Zhou Bo Wang School of Computer Science Northeast Electric Power UniversityJilin132000China Information and Communication Company State Grid Tianjin Electric Power CompanyTianjin300000China Graduate School of Science and Engineering Iwate UniversityMorioka020-8550Japan

The recycling of glass bottles can reduce the consumption of resources and contribute to environmental *** present,the classification of recycled glass bottles is difficult due to the many differences in specifications and *** paper proposes a classification algorithm for glass bottles that is divided into two stages,namely the extraction of candidate regions and the classification of *** the candidate region extraction stage,aiming at the problem of the large time overhead caused by the use of the SIFT(scale-invariant feature transform)descriptor in SS(selective search),an improved feature of HLSN(Haar-like based on SPP-Net)is *** integral graph is introduced to accelerate the process of forming an HBSN vector,which overcomes the problem of repeated texture feature calculation in overlapping regions by *** the classification stage,the improved SS algorithm is used to extract target *** target regions are merged using a non-maximum suppression algorithm according to the classification scores of the respective regions,and the merged regions are classified using the trained *** demonstrate that,compared with the original SS,the improved SS algorithm increases the calculation speed by 13.8%,and its classification accuracy is 89.4%.Additionally,the classification algorithm for glass bottles has a certain resistance to noise.

关键词： Classification of glass bottle HBSN feature improved selective search algorithm LightGBM

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