计算机与数字工程2026，Vol.54Issue(2)：577-582,6.DOI:10.3969/j.issn.1672-9722.2026.02.047
改进Faster R-CNN算法在箱体盘点中的应用
Application of Improved Faster R-CNN Algorithm in Box Inventory
康朝海 1唐贵鑫 1任伟建 1王树峰 2孙勤江3
作者信息
- 1. 东北石油大学电气信息工程学院 大庆 163318
- 2. 大庆油田有限责任公司第二采油厂 大庆 163414
- 3. 中海石油(中国)有限公司天津分公司 天津 300450
- 折叠
摘要
Abstract
Aiming at the problem of box counting in the dense list of storage shelves,an improved Faster R-CNN target detec-tion algorithm is proposed.Firstly,the EfficientNetb1 is used to replace VGG16 as the backbone feature extraction network,and the MBConv module composed of deep separable convolution and compression and activation network is used to replace the convolution neural network to extract the effective information of the image,to solve the problems of low accuracy and high parameter quantity of the detection network.Secondly,bilinear interpolation method is used to solve the error problem in pooling stage.Finally,in the re-gression prediction stage,the Swish activation function is used to replace the ReLU activation function to reduce the number of ne-crotic neurons.The experimental results show that the mAP value of the improved Faster R-CNN target detection algorithm reaches 66.92%,which is 30.93%higher than the original mAP value,and the model parameter quantity is reduced to 1/5 of the original pa-rameter quantity.In the detection image,the overall number and classification accuracy of box detection have been significantly im-proved,which proves the feasibility of the improved algorithm in the application of box inventory.关键词
计算机视觉/Faster R-CNN/EfficientNet/箱体识别/分类计数Key words
computer vision/Faster R-CNN/EfficientNet/box recognition/category count分类
信息技术与安全科学引用本文复制引用
康朝海,唐贵鑫,任伟建,王树峰,孙勤江..改进Faster R-CNN算法在箱体盘点中的应用[J].计算机与数字工程,2026,54(2):577-582,6.
