Deep object detection framework for automated quality inspection in assembly operations

Basamakis, Fotios Panagiotis; Bavelos, Angelos Christos; Dimosthenopoulos, Dimosthenis; Papavasileiou, Apostolis; Makris, Sotiris

doi:10.1016/j.procir.2022.10.068

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Object Detection Accuracy Hybrid Quality Inspection [72] 82% ResNet-101-FPN [73] 71.8% Deep Learning framework [74] 96% Proposed Framework 98%…”

Section: Existing Methodsmentioning

confidence: 99%

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Papavasileiou,

Nikoladakis,

Basamakis

et al. 2024

Applied Sciences

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Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.

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“…Object Detection Accuracy Hybrid Quality Inspection [72] 82% ResNet-101-FPN [73] 71.8% Deep Learning framework [74] 96% Proposed Framework 98%…”

Section: Existing Methodsmentioning

confidence: 99%

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Papavasileiou,

Nikoladakis,

Basamakis

et al. 2024

Applied Sciences

Self Cite

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“…The authors tested their approaches in 4 datasets called: Overall, Impurity, Overfill, Underfill and they achieved a mAP@0.50 scores of 82.00%, 70.00%, 97.00% and 80.00%, respectively. Basamakis et al 8 proposed a deep learning object detection framework able to detect correct, misaligned, and missing objects in scenes of the production line. Furthermore, the suggested architecture offers interfaces that enable the model's integration with a variety of manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

Object detection model-based quality inspection using a deep CNN

Chetoui¹,

Akhloufi²

2023

Sixteenth International Conference on Quality Control by Artificial Vision

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In the competitive world of the metal industry where companies have to offer quality products, quality control is crucial. However, it takes a considerable amount of time, especially if it is a manual process. Automatic Fault Detection (AFD) system reduces a lot of work for the companies, saves time, money and improves use of available resources. Deep learning can be efficiently used to develop such a AFD system. In this article, we present the development of deep learning (DL) algorithms for quality control. We trained State-of the-art DL (YOLO v8n, YOLO v8s, YOLO v8m, YOLO v8l and YOLO v8x) for a quality control task using a manually annotated dataset of 3 classes (neck scratch, scratch and bent) for 2 objects (Screw and Metal Nut). The results show very interesting scores for YOLO v8s with an mAP@0.50 of 90.60%, a precision of 100% and a recall of 94.0% for the 3 classes on average. We also compared the performance of these models with a popular DL model detector called Faster-RCNN x101 in order to confirm the performance of the developed models. The qualitative results show good detection of defects with different sizes (small, medium and large). Our proposition gives very interesting results to deploy an AFD system for metal industries.

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“…By subtracting the corresponding query region, a pixel-wise anomaly score is obtained, which is then used to detect defects [3]. A deep learning object detection framework is presented to detect correct, misaligned, and missing objects in complex scenes of the production line with a mAP (mean Average Precision) for multi-classes prediction by YOLOv4 model architecture [4]. I.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Assembly Defect Detection from Object Detection to Image Classification

Hsu,

Jhou,

et al. 2023

MSF

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The paper proposed a deep convolutional neural network together with image processing techniques to detect assembly defects of vehicle components in assembly lines. Traditional detection method such as automatic optical inspection is strongly affected by environmental variation coming from the changes of light source, transfer belt, and component type, therefore, complicated thresholds should be adjusted case by case. The proposed method tries to avoid these problems which is fast and straight forward with satisfactory detection accuracy compared to traditional method.

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Deep object detection framework for automated quality inspection in assembly operations

Cited by 11 publications

References 15 publications

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Object detection model-based quality inspection using a deep CNN

Enhancing Assembly Defect Detection from Object Detection to Image Classification

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