Adaptive Model Compression for Steel Plate Surface Defect Detection:An Expert Knowledge and Working Condition-Based Approach  

作　　者：Maojie Sun Fang Dong Zhaowu Huang Junzhou Luo 

机构地区：[1]School of Computer Science and Engineering,Southeast University,Nanjing 211189,China [2]Najing Iron and Steel Co.,Nanjing 210035,China

出　　处：《Tsinghua Science and Technology》2024年第6期1851-1871,共21页清华大学学报自然科学版（英文版）

基　　金：supported by the National Key R&D Program of China(No.2018AAA0100500);the National Natural Science Foundation of China(Nos.62232004 and 61632008);the Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201);the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China(No.93K-9);the Collaborative Innovation Center of Novel Software Technology and Industrialization;the Big Data Computing Center of Southeast University in China for providing the experiment environment and computing facility.

摘　　要：The steel plate is one of the main products in steel industries,and its surface quality directly affects the final product performance.How to detect surface defects of steel plates in real time during the production process is a challenging problem.The single or fixed model compression method cannot be directly applied to the detection of steel surface defects,because it is difficult to consider the diversity of production tasks,the uncertainty caused by environmental factors,such as communication networks,and the influence of process and working conditions in steel plate production.In this paper,we propose an adaptive model compression method for steel surface defect online detection based on expert knowledge and working conditions.First,we establish an expert system to give lightweight model parameters based on the correlation between defect types and manufacturing processes.Then,lightweight model parameters are adaptively adjusted according to working conditions,which improves detection accuracy while ensuring real-time performance.The experimental results show that compared with the detection method of constant lightweight parameter model,the proposed method makes the total detection time cut down by 23.1%,and the deadline satisfaction ratio increased by 36.5%,while upgrading the accuracy by 4.2%and reducing the false detection rate by 4.3%.

关 键 词：steel surface defect detection inference acceleration model compression expert knowledge PRUNING QUANTIZATION 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]