| 1 | 基于改进多目标模板匹配在铝材计数的应用显示文摘针对铝材入库计数过程存在效率低和耗时长的问题,提出了一种基于机器视觉和改进的图像金字塔的铝材计数方法。该方法应用Sobel边缘检测提取模板轮廓,采用加权最小二乘法进行直线拟合实现模板轮廓的优化处理,进而得到最优模板,改进后的图像金字塔算法实现了对图像模板匹配的快速计数。实验结果表明,所提出的方法应用于铝材入库计数,具有较高的召回率和较快的匹配速度,测试数据集的平均检测准确率达到98.4%,匹配效率相较于传统图像金字塔算法提升了37.0%。 | 高旭升 陈晓荣 王原野 王子旋 徐挺 | 2024 | 软件工程2024，27，1： | 0 |
| 2 | TO–YOLOX: a pure CNN tiny object detection model for remotesensing images显示文摘Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention.However,due to interference occasioned by density,overlap,and coverage,the tiny object detection in remote sensing images has always been a difficult problem.Therefore,we propose a novel TO–YOLOX(Tiny Object–You Only Look Once)model.TO–YOLOX possesses a MiSo(Multiple-in-Singleout)feature fusion structure,which exhibits a spatial-shift structure,and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images.TO–YOLOX utilizes an adaptive IOU-T(Intersection Over Uni-Tiny)loss to enhance the localization accuracy of tiny objects,and it applies attention mechanism Group-CBAM(group-convolutional block attention module)to enhance the perception of tiny objects in remote sensing images.To verify the effectiveness and efficiency of TO–YOLOX,we utilized three aerial-photography tiny object detection datasets,namely VisDrone2021,Tiny Person,and DOTA–HBB,and the following mean average precision(mAP)values were recorded,respectively:45.31%(+10.03%),28.9%(+9.36%),and 63.02%(+9.62%).With respect to recognizing tiny objects,TO–YOLOX exhibits a stronger ability compared with Faster R-CNN,RetinaNet,YOLOv5,YOLOv6,YOLOv7,and YOLOX,and the proposed model exhibits fast computation. | Zhe Chen Yuan Liang Zhengbo Yu Ke Xu Qingyun Ji Xueqi Zhang Quanping Zhang Zijia Cui Ziqiong He Ruichun Chang Zhongchang Sun Keyan Xiao Huadong Guo | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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