To address the influence of appearance interference on structural-state judgment in geometric deformation detection of logistics packaging boxes, the work aims to propose an RGB-D vision-based detection method. A detection pipeline consisting of a single-class box candidate localization front end and an RGB-D geometric verification back end was constructed. The front end obtained candidate box regions from RGB images only, while the back end determined the authenticity of deformation based on depth geometric information. To avoid reference baseline deviation caused by abnormal depth values in the target measurement region, an Adaptive Doughnut Reference Plane (ADRP) method was proposed. By spatially separating the outer-ring reference region from the central measurement region and introducing a center-ring depth consistency judgment, ADRP adaptively selected the reference depth and constructed a local reference plane. On the frozen test set, the system achieved end-to-end performance of Precision=1.000, Recall=0.750, FAR=0.000, and F1=0.857 at a low-false-alarm operating point. Comparative experiments and candidate-box perturbation tests showed that, under manually anchored regions of interest, ADRP achieved higher Recall and F1 than fixed-depth filtering (Recall=0.036, F1=0.069) and global RANSAC (Recall=0.714, F1=0.833), while maintaining FAR=0.000. The perturbation test further indicated that the system showed tolerance to small localization offsets. Under the current static top-view RGB-D acquisition condition and frozen test set, the proposed method demonstrates a low-false-alarm detection capability and provides an RGB-D vision reference for automatic geometric deformation detection of logistics packaging boxes.
Key words
logistics packaging box /
Red-Green-Blue-Depth (RGB-D) vision /
geometric deformation detection /
candidate region localization /
geometric verification /
Adaptive Doughnut Reference Plane (ADRP)
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] MA Y, YIN J, HUANG F, et al.Surface Defect Inspection of Industrial Products with Object Detection Deep Networks: A Systematic Review[J]. Artificial Intelligence Review, 2024, 57: 333.
[2] WAHYUDI V, ZIEGLER C C, FRIESS M, et al.A Computer Vision System for Recognition and Defect Detection for Reusable Containers[J]. Machine Vision and Applications, 2025, 36: 39.
[3] WU Y, LU Y.An Intelligent Machine Vision System for Detecting Surface Defects on Packing Boxes Based on Support Vector Machine[J]. Measurement and Control, 2019, 52(7-8): 1102-1110.
[4] YANG M, LI D, LUO P, et al.YOLOv5-S-G-B: A Lightweight Intelligent Detection Model for Cardboard Surface Defects[J]. Signal, Image and Video Processing, 2024, 18: 6997-7011.
[5] AMERI R, HSU C C, SHAMSHIRBAND S, et al.A Systematic Review of Deep Learning Approaches for Surface Defect Detection in Industrial Applications[J]. Engineering Applications of Artificial Intelligence, 2024, 130: 107717.
[6] 宗国浩, 张明琰, 王锐, 等. 卷烟包装外观缺陷数据集构建及深度学习检测技术研究[J]. 包装工程, 2024, 45(5): 135-143.
ZONG G H, ZHANG M Y, WANG R, et al.Construction of Appearance Defect Dataset for Cigarette Package and Research on Deep Learning Detection Technology[J]. Packaging Engineering, 2024, 45(5): 135-143.
[7] 叶宇星, 孙志锋, 马风力, 等. 基于改进YOLOv5s的腌制蔬菜真空包装缺陷检测[J]. 包装工程, 2023, 44(9): 45-53.
YE Y X, SUN Z F, MA F L, et al.Vacuum Packaging Defect Detection of Pickled Vegetables Based on Improved YOLOv5s[J]. Packaging Engineering, 2023, 44(9): 45-53.
[8] CHEN A, LI X, HE T, et al.Advancing in RGB-D Salient Object Detection: A Survey[J]. Applied Sciences, 2024, 14(17): 8078.
[9] HE X, MATSUMARU T.Estimation of Flat Object Deformation Using RGB-D Sensor for Robot Reproduction[J]. Sensors, 2021, 21(1): 105.
[10] DU J, TAO C Y, CAO X M, et al.3D Vision-Based Anomaly Detection in Manufacturing: A Survey[J]. Frontiers of Engineering Management, 2025, 12(2): 343-360.
[11] CAVALIERE G, LANZA O, BORGIANNI Y, et al.Deep Learning-Supported Machine Vision-Based Hybrid System Combining Inhomogeneous 2D and 3D Data for the Identification of Surface Defects[J]. Production & Manufacturing Research, 2024, 12(1): 2378199.
[12] LI Y, LI W, DARWISH W, et al.Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors[J]. Remote Sensing, 2020, 12(2): 320.
[13] KHOSHELHAM K, OUDE ELBERINK S J. Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications[J]. Sensors, 2012, 12(2): 1437-1454.
[14] SAPKOTA R, FLORES-CALERO M, QURESHI R, et al.YOLO Advances to Its Genesis: A Decadal and Comprehensive Review of the You Only Look Once (YOLO) Series[J]. Artificial Intelligence Review, 2025, 58: 274.
[15] FISCHLER M A, BOLLES R C.Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
[16] ROUSSEEUW P J, CROUX C.Alternatives to the Median Absolute Deviation[J]. Journal of the American Statistical Association, 1993, 88(424): 1273-1283.
[17] CHANLLON. Corrugated Cardboard Boxes Dataset[EB/OL].[2026-06-09]. https://github.com/chanllon/corrugated-cardboard-boxes-dataset/tree/master.
PDF(1572 KB)
