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作 者:WANG Baomin DING Hewei TENG Fei LIIU Hongqin 王保民;丁贺伟;滕飞;刘洪芹
出 处:《Journal of Shanghai Jiaotong university(Science)》2025年第2期309-318,共10页上海交通大学学报(英文版)
摘 要:Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
关 键 词:conveyer belts with steel rope cores DAMAGE X-ray image image detection improved fully convo-lutional one-stage object detection(FCOS)algorithm
分 类 号:TD528[矿业工程—矿山机电] TP391.41[自动化与计算机技术—计算机应用技术]
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