Container Damage Identification Based on Fmask-RCNN

Li, Xueqi; Liu, Qing; Wang, Jinbo; Wu, Ji-Wei

doi:10.1007/978-981-15-7670-6_2

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Faster R-CNN has already been successfully tested for different damage detection tasks, e.g., road damage detection [18] and classification [19]. A particular version of R-CNN, Mask R-CNN, is used for the damage segmentation of containers [20] and cars [21], which further suggests the effectiveness of the algorithm for this type of task.…”

Section: State Of the Artmentioning

confidence: 99%

Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals

Cimili,

Voegl,

Hirsch

et al. 2024

Sustainability

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Efficient damage detection of trailers is essential for improving processes at inland intermodal terminals. This paper presents an automated damage detection (ADD) algorithm for trailers utilizing ensemble learning based on YOLOv8 and RetinaNet networks. The algorithm achieves 88.33% accuracy and an 81.08% F1-score on the real-life trailer damage dataset by leveraging the strengths of each object detection model. YOLOv8 is trained explicitly for detecting belt damage, while RetinaNet handles detecting other damage types and is used for cropping trailers from images. These one-stage detectors outperformed the two-stage Faster R-CNN in all tested tasks within this research. Furthermore, the algorithm incorporates slice-aided hyper inference, which significantly contributes to the efficient processing of high-resolution trailer images. Integrating the proposed ADD solution into terminal operating systems allows a substantial workload reduction at the ingate of intermodal terminals and supports, therefore, more sustainable transportation solutions.

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Section: State Of the Artmentioning

confidence: 99%

Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals

Cimili,

Voegl,

Hirsch

et al. 2024

Sustainability

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show abstract

“…In addition, other existing networks have been proposed, such as Faster-RCNN (Ren et al, 2017), with a binary search tree to find the container identifier (Zhiming et al, 2019). A variation of Mask-RCNN has also been used in damage detection, giving place to Fmask-RCNN (Li et al, 2020) which introduces changes in the backbone, fusions in the FPN and multiple fully connected layers. Bahrami et al (Bahrami et al, 2020) also introduced neural networks architectures to detect corrosion in containers.…”

Section: Related Workmentioning

confidence: 99%

Methodology for generating synthetic labeled datasets for visual container inspection

Delgado,

Cortés,

García

et al. 2023

Transportation Research Part E: Logistics and Transportation Re

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“…They used ResNet-101-FPN to modify the RCNN mask on the BBBC038v1 picture, which contains tiny nuclei images. Another mask RCNN implementation was carried out by the authors of the following paper [21]. They have discovered how to automatically identify dents, convex, hole, damage, and distortion in containers using Fmask-RCNN.…”

Section: Background Studymentioning

confidence: 99%

Data Augmentation Technique to Expand Road Dataset Using Mask RCNN and Image Inpainting

Saha¹,

Ahmed²,

Ahmed³

et al. 2021

AJCT

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A popular method for training a machine learning model is to use a data-driven approach. This research contributes to expanding the dataset of urban road images without vehicles. Using this method will benefit a variety of existing and new research projects that require an empty road to train their data-driven model. To achieve the desired result, the method combines image segmentation and image inpainting. The model detects the vehicle with Mask RCNN and removes the detected object with image inpainting. Morphological transformation was used to improve the method's efficiency. Using dilation operation of morphological transformation, the mask generated from mask RCNN is enlarged. The results of the experiment support the method's efficacy.

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Container Damage Identification Based on Fmask-RCNN

Cited by 6 publications

References 5 publications

Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals

Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals

Methodology for generating synthetic labeled datasets for visual container inspection

Data Augmentation Technique to Expand Road Dataset Using Mask RCNN and Image Inpainting

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