TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases

Dörr, Laura; Brandt, Felix; Naumann, Alexander; Pouls, Martin

doi:10.1007/978-3-030-92659-5_35

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Specialised algorithms based on machine learning have recently been implemented in order to recognise packaging boxes on pallets. For example, in the study [7] shows a Neural Network (NN) called TetraPackNet that is used to detect corner points from the images of the side faces of pallets. This approach was designed as an extension of a well-known NN called CornerNet [8].…”

Section: Related Workmentioning

confidence: 99%

“…Unlike the works described above, our method is focused on detecting and recognising packaging loaded on pallets, and not on lumber platforms as described in [12,14,16]. Moreover, we use video sequences captured from an RGB camera with a bird's eye view configuration rather than a side view to detect the packing, as occurred in [7,9]. Our proposal is based on 2D techniques rather than 3D processing, as in [12,19,20], signifying that we require less computational complexity and there is less dependency on the camera type and its location.…”

Section: Related Workmentioning

confidence: 99%

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MOSPPA: monitoring system for palletised packaging recognition and tracking

Castaño-Amorós

Fuentes²,

Gil³

2023

Int J Adv Manuf Technol

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The paper industry manufactures corrugated cardboard packaging, which is unassembled and stacked on pallets to be supplied to its customers. Human operators usually classify these pallets according to the physical features of the cardboard packaging. This process can be slow, causing congestion on the production line. To optimise the logistics of this process, we propose a visual recognition and tracking pipeline that monitors the palletised packaging while it is moving inside the factory on roller conveyors. Our pipeline has a two-stage architecture composed of Convolutional Neural Networks, one for oriented pallet detection and recognition, and another with which to track identified pallets. We carried out an extensive study using different methods for the pallet detection and tracking tasks and discovered that the oriented object detection approach was the most suitable. Our proposal recognises and tracks different configurations and visual appearance of palletised packaging, providing statistical data in real time with which to assist human operators in decision-making. We tested the precision-performance of the system at the Smurfit Kappa facilities. Our proposal attained an Average Precision (AP) of 0.93 at 14 Frames Per Second (FPS), losing only 1% of detections. Our system is, therefore, able to optimise and speed up the process of logistic distribution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MOSPPA: monitoring system for palletised packaging recognition and tracking

Castaño-Amorós

Fuentes²,

Gil³

2023

Int J Adv Manuf Technol

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“…In a follow-up work, Dörr et al [Dör+21] presented a novel approach for the side face detection problem. They extend CornerNet [LD18] to support the detection of arbitrary four-cornered polygons, instead of axis-aligned bounding boxes.…”

Section: Completeness and Occupancymentioning

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)

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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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“…Algorithms derived from computer vision and AI are employed to decode labels, barcodes, and QR codes, read any text, and identify objects [8]. Regarding the recognition of boxes, numerous academic research groups have conducted studies that achieved significant advancements in the identification of packing structures [1,4,[9][10][11][12][13]. A multi-step image processing pipeline was suggested by Dörr et al for automated packaging structure recognition [1].…”

Section: Introductionmentioning

confidence: 99%

“…The study in [12] discusses automating the unloading of containers and suggests a technique that combines a Mask-RCNN algorithm, which is based on deep learning, with 3D depth measurements to detect targets. In [13], TetraPackNet (a unique model) is presented for segmenting objects by four vertices rather than bounding boxes or pixel masks. The model is used for recognizing the structure of logistics packaging.…”

Section: Introductionmentioning

confidence: 99%

EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems

Firdiantika,

Lee,

Bhattacharyya

et al. 2024

Applied Sciences

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Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models.

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TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases

Cited by 6 publications

References 9 publications

MOSPPA: monitoring system for palletised packaging recognition and tracking

MOSPPA: monitoring system for palletised packaging recognition and tracking

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

EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems

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