A Comparative Assessment of Parcel Box Detection Algorithms for Industrial Applications

Fontana, Ernesto; Zarotti, William; Rizzini, Dario Lodi

doi:10.1109/ecmr50962.2021.9568825

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…3D Object Detection: Plane segmentation is used for detecting parcels [Hu+21] and pallets [Xia+17; MF18]. Moreover, clustering approaches [FZL21] and deep learningbased pointcloud classification [BBS21] have been used.…”

Section: Methodological Categorizationmentioning

confidence: 99%

See 1 more Smart Citation

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Naumann

Dörr

Salscheider

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Computer vision applications in transportation logisticsand warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential. All literature is categorized w.r.t. the application, i.e. the task it tackles and w.r.t. the computer vision techniques that are used. Regarding applications, we subdivide the literature in two areas: Monitoring, i.e. observing and retrieving relevant information from the environment, and manipulation, where approaches are used to analyze and interact with the environment. In addition to that, we point out directions for future research and link to recent developments in computer vision that are suitable for application in logistics. Finally, we present an overview of existing datasets and industrial solutions. We conclude that while already many research areas have been investigated, there is still huge potential for future research. The results of our analysis are also available online at https://a-nau.github.io/cv-in-logistics.1 Verband der Automobilindustrie e.V. (VDA), see https://www.vda.de/en.

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Section: Methodological Categorizationmentioning

confidence: 99%

“…While the previous works only used RGB images, Fontana et al [FZL21] present an approach for parcel detection based on RGBD data. They compare an approach that combines a Mask R-CNN with post-processing with a clustering approach on the depth data.…”

Section: Item Recognitionmentioning

confidence: 99%

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Naumann

Dörr

Salscheider

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

show abstract

“…The main object detection algorithms used are YOLO (Redmon et al [ 17 ]) and R-CNN (He et al [ 18 ]). YOLO is a grid-based approach that predicts boundaries and selects the highest-class probability after an object passes through a CNN, and R-CNN classifies the pixels that constitute the object in the identified boundaries, proposes the potential position of the object using a neural network, and classifies and detects the object based on the proposed area [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. In the case of Zhao et al [ 19 ], they propose a planar parcel detection method using R-CNN reinforcement.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading

Kim,

Truong,

Lee

et al. 2024

Sensors

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Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

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Vision-centric 3D point cloud technique and custom gripper process for parcel depalletisation

Kim,

Lee,

Kim

et al. 2024

J Intell Manuf

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A Comparative Assessment of Parcel Box Detection Algorithms for Industrial Applications

Cited by 7 publications

References 14 publications

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading

Vision-centric 3D point cloud technique and custom gripper process for parcel depalletisation

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