计算机工程与应用2025，Vol.61Issue(4)：323-330,8.DOI:10.3778/j.issn.1002-8331.2310-0102
基于旋转框定位的拆垛箱体目标检测
Object Detection of Depalletizing Box Based on Rotating Frame Location
摘要
Abstract
In order to solve the problems of dense stacking,messy arrangement and random incoming materials in the logis-tics field,which makes it difficult to accurately locate the depalletizing box,a lightweight and precise positioning method for box based on rotating frame location is proposed.Based on the one-stage detection network YOLOv7-tiny,a feature selection module integrating deformable convolution is used to improve the box deformation modeling and enhance the extraction of contextual features,specifically addressing the problem of densely arranged box targets.Coordinate attention(CA)mechanism is integrated to enhance the expression of target features.The long-edge representation of the rotating box is used to accurately represent the box target frame,and the Kullback-Leibler divergence(KLD)between Gaussian distri-butions is utilized for the loss function to realize the exact regression of the box boundary.The experimental results show that the proposed method achieves 85.25%AP75 on the box dataset,which is 7.85 percentage points higher than the base-line model,the average pixel distance of the center point of the box is less than 3 pixels,and the average offset angle is less than 4 degrees,and the detection speed reaches 26.52 FPS on the basis of slightly increasing the number of model parameters.The proposed method can effectively improve the positioning accuracy of the depalletizing box and meet the actual destacking needs.关键词
旋转目标检测/箱体定位/YOLOv7-tiny/特征融合/注意力机制Key words
rotating object detection/box location/YOLOv7-tiny/feature fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
张相胜,程嘉宝,顾斌杰..基于旋转框定位的拆垛箱体目标检测[J].计算机工程与应用,2025,61(4):323-330,8.基金项目
国家自然科学基金(51961125102). （51961125102）
