An APF-ACO algorithm for automatic defect detection on vehicle paint

Xu, Jiabin; Zhang, Jindong; Zhang, Kunpeng; Liu, Tong; Wang, Donghui; Wang, Xue

doi:10.1007/s11042-020-09245-2

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…Recently, defect object detection based on deep learning can achieve rapid end-to-end classification and location of defects [44]. Zhang et al [11] proposed the improved MobileNet-SSD algorithm for automatic detection of paint defects.…”

Section: Defect Detectionmentioning

confidence: 99%

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Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

Zhang,

Wang,

Guo

et al. 2023

IET Image Processing

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Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors’ scheme, which achieves superior performance.

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Section: Defect Detectionmentioning

confidence: 99%

“…Recently, defect object detection based on deep learning can achieve rapid end‐to‐end classification and location of defects [44]. Zhang et al.…”

Section: Related Workmentioning

confidence: 99%

Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

Zhang,

Wang,

Guo

et al. 2023

IET Image Processing

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“…Edris et al [5] first segmented the potential defective regions after processing the automotive body panel images using morphological methods and then used a multilayer perceptron for defect recognition and classification. Xu et al [6] proposed ant colony optimization based on the features of automotive paint and designed an interference elimination algorithm based on HSV color space to improve the detection of automotive paint defects. Cheng et al [7] used a morphology-based image enhancement method and a graph theory-based image segmentation method to locate paint defects and employed a support vector machine for paint defect recognition and classification.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised method for visual detection of automotive paint defects

Jiang,

Chen,

et al. 2024

Meas. Sci. Technol.

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Automotive paint defect detection plays a crucial role in the automotive production process. Current research on visual defect detection methods is mainly based on supervised learning, which requires a large number of labeled image samples for model training. The labeling work is not only time consuming but also expensive, seriously hindering the testing and application of these models in practice. To address this issue, this study proposes a new method for automotive paint defect detection based on a semi-supervised training strategy. First, a semi-supervised automotive paint defect detection framework, which can use labeled and unlabeled samples to reduce the cost of data labeling effectively, is presented. Then, a spatial pyramid pooling fast external attention module that introduces an external attention mechanism is proposed to improve the traditional YOLOv7 network structure, called YOLOv7-EA, to obtain good detection performance. This network acts as a detector to generate high-quality pseudo labels for the unlabeled samples, providing additional data to train the model; meanwhile, it performs the final detection task. Lastly, a Wise-intersection over union loss function that considers the quality of the anchor box is introduced to reduce the interference of low-quality samples and improve the convergence speed and detection accuracy of the model. Using this method, we can accomplish the task of automotive paint defect detection with a small number of labeled image samples. Experimental results on the automotive paint defect dataset show that mean average precision (mAp)@.5, mAp@.75, and mAp@.5:.95 are superior to other methods under the condition of 10% and 15% labeled data, achieving good defect detection performance.

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“…Exterior vehicle inspection and defect monitoring have evolved significantly over the years, driven by advancements in computer vision and deep learning with an increasing focus on road safety and efficiency [ 1 , 2 , 3 , 4 , 5 , 6 ]. These technologies transform the field of vehicle exterior inspection by automating defect detection, classification and segmentation processes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges

El Saer,

Grammatikopoulos,

Sfikas

et al. 2024

Sensors

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Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

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An APF-ACO algorithm for automatic defect detection on vehicle paint

Cited by 17 publications

References 30 publications

Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

Semi-supervised method for visual detection of automotive paint defects

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges

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