Tire Images Research Articles

Tire Texture Monitoring (VGG 19 VS Efficient Net b7)

Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealistic renderings. Our results demonstrate remarkable improvements in texture quality and visual realism compared to traditional methods. The rendered images exhibit finer details and more accurate representations of the tire’s tread patterns and material properties. This research contributes to the field of computer graphics by presenting a novel application of deep learning techniques to a specific industrial need, paving the way for future advancements in high-quality rendering of complex tire textures. Key Words: VGG19,Photorealistic rendering, deep learning.

Anomaly Detection of Tire Tiny Text: Mechanism and Method

Due to the variety of tire specifications and poor contrast, it is difficult for ordinary 2D vision-based methods to accurately and autonomously detect anomalies in the tire text embossed on the tire sidewalls. In this paper, a 3D vision-based autonomous scanning mechanism and method is proposed, including three core components: a three-degree-of-freedom (3-DoF) robotic arm, a high-precision turntable and a composite vision probe. Based on the designed vision probe, the mechanism can autonomously scan the tire sidewall through a series of optimal viewpoints to obtain high-quality tire images even in the absence of tire CAD models. To obtain the optimal viewpoints, an information-driven viewpoint planning method is proposed. Combined with the real-time viewpoint adjustment strategy, the viewpoint of the vision probe is dynamically fine-tuned, which can effectively balance the scanning precision and motion cost. In the text anomaly detection phase, a three-stage tire text anomaly detection method based on deep learning and statistics strategy is proposed, which can accurately detect and recognize tire text, identify sub-mold categories, and judge text anomalies. Experimental results show that the proposed mechanism and method perform well in terms of accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 96%) and efficiency (time per lap for a tire is less than 9 s); it shows that the proposed mechanism and method have certain industrial application value and can be applied to tire quality inspection of on-line production. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Tire text may be incorrectly embossed during tire manufacturing. Due to the wide variety of tire specifications, poor contrast, and thin text strokes, it is difficult to detect abnormal tire text. In this paper, a 3D vision-based mechanism and method is developed to address this issue. Using a specially designed composite vision probe and scanning method, the mechanism can observe tiny text information on tires of different specifications from multiple angles and directions. By using our tire text anomaly detection method based deep learning and statistics strategy, the mechanism can accurately and robustly detect the region and content of abnormal text. The mechanism is efficient (with a detection time of less than 9 s per lap for one tire) and accurate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 96%), indicating that it has great potential in the first inspection or quality inspection step of tire manufacturing in industrial scenarios.

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Ensuring tire quality is crucial in the manufacturing industry, particularly for race cars, where defective tires present a significant safety risk. Visual inspection for defects in tires is crucial; however, identifying defects in complex, textured tires has been proven to be a challenging task. This paper tackles this challenge by introducing XAFCNN, an Explainable Attention-based Fused Convolutional Neural Network for tire defect detection. XAFCNN’s novel architecture, including a Special Attention Module (SAM) and custom CNN structure, coupled with Grad-CAM visualization, prevents overfitting, enhances local feature mapping, enables detection of small defects, and proffers valuable insights into the model’s reasoning, enabling confident interpretation of its predictions. The model was trained on a dataset from a leading global tire manufacturer, including 38,710 x-ray images of defective tires and 83,985 defect-free tire images, covering 15 defect types and 50 design patterns. The results demonstrate the model’s exceptional performance compared to literature, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. This research, with its substantial dataset and high-performing model, advances automated tire defect detection, satisfying the industry’s need for accurate and reliable inspections, ultimately enhancing human safety.

Factors affecting the accuracy of a computer vision-based vehicle weight measurement system

Vehicle weight is an important element of traffic data as it affects both traffic safety and service life of infrastructures. Traditional techniques for vehicle weight measurement, such as static weighing and weigh-in-motion, are costly to maintain and have limited service life. Thus, the non-contact vehicle weight measurement has emerged as a promising alternative. This study proposes a computer vision (CV)-based method for vehicle weight measurement and the factors affecting its accuracy are analyzed. The process of CV-based weight measurement involves the capturing of tire (tyre) images, processing them to extract tire edges, calculating vertical tire deformations, determining actual tire pressures, and using the tire mechanics model to calculate the weight of each tire and the gross weight of the vehicle. Parametric studies are conducted to study the effects of factors such as distance between camera and tire, camera parameters, illumination, background, and vehicle speed on measurement accuracy. The results indicate that high accuracy is achieved under optimal conditions, such as short distance, appropriate camera parameters, normal illumination, large feature difference between tires and background, and low speed. In application, the identification error of gross vehicle weight is within 15% at 95% confidence interval, which is comparable to a weigh-in-motion system.

Noncontact measurement of tire deformation based on computer vision and Tire-Net semantic segmentation

Tire deformation is a contributor to vehicle driving safety and an important parameter reflecting the vehicle-road/bridge interaction. Existing methods for measuring tire deformation require measuring devices in contact with the tires, which has many limitations in practice. Thus, the present study proposes a noncontact measurement method of tire deformation based on computer vision and deep learning techniques. Firstly, the diverse dataset of tire images is established based on tire images collected from roadside cameras. Next, a new semantic segmentation Tire-Net is developed to segment the tire images. Then, the quantification algorithm including the subpixel-level edge detection, key point positioning, and scale factor determination is proposed to calculate the physical value of tire deformation. Finally, field tests are carried out on the tires of cars, buses, light trucks, and heavy trucks to verify the proposed method. The results show that it performs well as a means of measuring tire deformation.

Research on tire crack detection using image deep learning method

Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection method was designed. This paper improves the traditional ShuffleNet and proposes an improved ShuffleNet method for tire image detection. The research results are compared with the five methods of GoogLeNet, traditional ShuffleNet, VGGNet, ResNet and improved ShuffleNet through tire database verification. The experiment found that the detection rate of tire debris defects was 94.7%. Tire defects can be effectively detected, which proves the robustness and effectiveness of the improved ShuffleNet, enabling drivers and tire manufacturers to save labor costs and greatly reduce tire defect detection time.

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%).

Tire appearance defect detection method via combining HOG and LBP features

Tire appearance defect detection based on machine vision is an effective technology to improve the tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers are paying attention to this technology. However, tires are characterized by single block colors and various defects. It is a great challenge to accurately detect tire appearance defects. To complete the task of detecting tire defects, this paper presents a novel tire appearance defect detection method via combining histogram of oriented gradients (HOG) and local binary pattern (LBP) features. First, we construct a tire image dataset to provide defective and normal tire images. Then, histogram of oriented gradients and local binary pattern features of tire images are, respectively, extracted and used to train the support vector machine (SVM) classifier. Finally, the support vector machine classifier calculates the prediction scores of the test images via combining the histogram of oriented gradients and local binary pattern features. These scores can be utilized to determine whether the test image is a defective or a normal tire image, and the goal of tire appearance defect detection is achieved. Conducted on the tire image dataset, our method has verified the effectiveness of detecting tire detects, and the mean accuracy is improved more than 1.6% than the algorithm that only uses the histogram of oriented gradients or local binary pattern feature. The experimental results demonstrate that the combination of HOG and LBP features can increase tire appearance defect detection accuracy.

Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks

Purpose: In this study, the authors designed an algorithm based on convolutional neural networks that can automatically assess tire quality.Design/methodology/approach: The proposed algorithm is built through several stages as follows. In the first stage, the tire images, which are the input of the designed algorithm, are acquired. Further, the acquired images are divided into two sets, namely training and testing sets. The training set contains tire images used in the training phase of several convolutional neural networks (CNN) architectures such as ResNet-50, MobileNetV2, Inception V3, and DenseNet-121. The training phase is carried out in a number of epochs, and at each epoch, the cross entropy loss function will be calculated which expresses the performance of the CNN architecture in classifying tire images. For this reason, the training stage requires a label or reference that shows the feasibility of the tires displayed in each image.Findings/result: In the testing phase, trained CNN architectures are used to classify tire images from the test set. Classification performance in the test set is also expressed in terms of cross-entropy loss function value. In addition, the accuracy value has also been calculated which shows the percentage of the number of tire images that are successfully classified correctly to the total number of tire images in the test set, namely the DenseNet-121 model has the best accuracy of 92.62%.Originality/value/state of the art: Given the high accuracy achieved by our algorithm, this work can be used as a reference by other researchers, specifically to benchmark their tire quality classification methods developed in the future.

IDENTIFIKASI KELAYAKAN KARET BAN DENGAN MODEL CNN RESNET-50

In this study, the authors designed an algorithm based on a convolutional neural network that is capable of automatically classifying tire rubber eligibility according to the appearance of the tire on the tire image. The proposed algorithm will be built through several stages as follows. In the first stage, tire image acquisition will be carried out which will be the input of the designed algorithm. Furthermore, the acquired image will be divided into two sets, namely training and testing sets. The training set contains tire images that will be used at the training stage of several convolutional neural network architectures to be able to and classify them to the appropriate level of feasibility. The training phase will be carried out in a number of epohs, and at each epoh, the cross entropy loss function value will be calculated which expresses the performance of the convolutional neural network architecture in classifying tire images.&#x0D; In this study, the author has designed an algorithm based on deep learning that is capable of automatically classifying tire eligibility. The proposed algorithm has been built through several stages such as tire image acquisition, training of several CNN models, especially ResNet-50. The CNN architecture test is trained to classify tire images from the test set. In addition, the accuracy value has also been calculated which shows the percentage of the number of tire images that are successfully classified correctly to the total number of tire images in the test set, which is an accuracy of 88.31%.

Tire Contact Force Equations for Vision-Based Vehicle Weight Identification

Overloaded vehicles have a variety of adverse effects; they not only damage pavements, bridges, and other infrastructure but also threaten the safety of human life. Therefore, it is necessary to address the problem of overloading, and this requires the accurate identification of the vehicle weight. Many methods have been used to identify vehicle weights. Most of them use contact methods that require sensors attached to or embedded in the road or bridge, which have disadvantages such as high cost, low accuracy, and poor durability. The authors have developed a vehicle weight identification method based on computer vision. The methodology identifies the tire–road contact force by establishing the relationship using the tire vertical deflection, which is extracted using computer vision techniques from the tire image. The focus of the present paper is to study the tire–road contact mechanism and develop tire contact force equations. Theoretical derivations and numerical simulations were conducted first to establish the tire force–deformation equations. The proposed vision-based vehicle weight identification method was then validated with field experiments using two passenger cars and two trucks. The effects of different tire specifications, loads, and inflation pressures were studied as well. The experiment showed that the results predicted by the proposed method agreed well with the measured results. Compared with the traditional method, the developed method based on tire mechanics and computer vision has the advantages of high accuracy and efficiency, easy operation, low cost, and there is no need to lay out sensors; thus, it provides a new approach to vehicle weighing.

Detection of Bubble Defects on Tire Surface Based on Line Laser and Machine Vision

In order to eliminate driving dangers caused by tire surface bubbles, the detection method of bubble defects on tire surfaces based on line lasers and machine vision is studied. Since it is difficult to recognize tire surfaces directly through images, line laser scanning is used to obtain tire images. The filtering method and morphology method are combined to preprocess these images. The gray centroid method is adopted to extract the center of the laser stripe, and then the algorithm to determine the positions of bubble defects on tire surfaces is proposed. According to the geometric characteristics of tire bubbles, the coordinates of starting points, ending points, and rough positions of vertices are determined. Then, the ordinates of the laser center with sub-pixel accuracy near bubble vertices are discretely magnified. The mask made of Gaussian function is convoluted with the magnified region, and the maximum value is obtained. Furthermore, the position of bubble vertices can be accurately extracted. The denoising effects of different methods for images are compared through experiments, and different positions of bubbles are detected. Experimental results show that the detection accuracy of this method is up to 93%, which is much higher than other methods. Experiments verify that the proposed method is effective for detecting tire surface bubbles.

TireNet: A high recall rate method for practical application of tire defect type classification

Tire enterprises often use X-ray photographs to inspect tire defects, which is still in the stage of manual inspection. Existing methods have achieved a lot of success in tire defect detection, but have not yet reached the application level requirements. It is hard for unsupervised methods to handle the complex specs and patterns of tires, while the supervised methods suffered from a lack of enough data. In this paper, an end-to-end method (TireNet) is proposed for the practical application of automatic tire defect detection with X-ray images. Object detection models are introduced as the baselines, it is found that the recall rate (missed detection rate) cannot meet the application-level needs of enterprises, since tire defects are quite different from general objects. Inspired by the features of tire X-ray images, which are periodic, the Siamese network is used in the new model as part of the downstream classifier to capture the defective features. Additionally, 120,000 labeled tire images (including qualified tires) were set up and collected as datasets. The experimental results of this method reach a higher recall rate compared to Faster R-CNN, SSD, and YOLO in the labeled datasets. At present, the model has been successfully deployed to the quality inspection department of enterprises and partly replaced artificial jobs. The application results show that the missing rate of TireNet is 0.17%, which is lower than that of the artificial 2.4%. With the help of a deployed assistant detection system, the tire defect detection rate increased by more than 100%, and the tire refund rate decreased by approximately 20%.

Automobile tire life prediction based on image processing and machine learning technology

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Vehicle Tire Text Reader: Text Spotting and Rectifying for Small, Curved, and Rotated Characters

Tire burst of vehicles is one of the main causes of highway accidents. Furthermore, tire aging is the most important cause of tire burst. It will be of great significance to traffic safety, through identifying the tire text code (TTC) to determine whether or not a tire is over the service life. In this article, a coarse-to-fine method of detection and rectification for TTC is proposed. First, text regions are located using the deep convolutional neural network (DCNN) model. Second, through fitting the tire circle curve, the close-up image of TTC is rotated to a positive direction. Third, a text boundary points’ generation strategy is proposed, which is based on the rotating bounding box of each character. Fourth, the curved text is rectified and input to the DCNN recognition model. On the tire image dataset, the detection accuracy of TTC and characters is 94% and 96.5% respectively, and the recognition accuracy is 94.5%. Through additional experimental results, it is proven that the proposed method has strong robustness to illumination and camera shooting angle, which can meet the real-time detection and recognition requirements as well.

Intuitionistic fuzzy negations and their use in image classification

In this paper, the problem of classification of images is discussed. Our specific problem is that we need to classify tire images into selected classes. The classes are characterized by some patterns. In the first step images are represented as the vectors. Then the membership and non-membership value to each coordinate of the vector is calculated and the theory of intuitionistic fuzzy sets is used. In [7] the classification of images was performed with respect to the valued of so called Sim function, which was defined as a ratio of distance between pattern data and image data and distance between pattern data and the complement of image data. The complement of image data was obtained by using specific intuitionistic fuzzy negation. In [2] a list of 53 intuitionistic fuzzy negations was presented. We have decided to use some of these negations to improve the results of classification.

Tire Defect Detection Using Fully Convolutional Network

A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN) for detecting defects in tire X-ray images. Owing to the capability of pixel-wise prediction of FCN, the location, and segmentation of defects are completed simultaneously. The network architecture used in the method mainly consists of three phases. The first phase is a traditional deep network, which is used to extract the feature of tire images, and feature maps are obtained at the last layer. By replacing fully connected layers into convolution layers, final feature maps retain sufficient spatial information. By adding up-sampling layers, in the second phase, outputs with the same size as the original image can be generated. After the first two phases, we develop the coarse segmentation results and refine them through fusing multi-scale feature maps. The experimental results show that the proposed method can accurately locate and segment defects in tire images.

High-Precision Detection of Defects of Tire Texture Through X-ray Imaging Based on Local Inverse Difference Moment Features.

Automatic defect detection is an important and challenging issue in the tire industrial quality control. As is well known, the production quality of tire is directly related to the vehicle running safety and passenger security. However, it is difficult to inspect the inner structure of tire on the surface. This paper proposes a high-precision detection of defects of tire texture image obtained by X-ray image sensor for tire non-destructive inspection. In this paper, the feature distribution generated by local inverse difference moment (LIDM) features is proposed to be an effective representation of tire X-ray texture image. Further, the defect feature map (DFM) may be constructed by computing the Hausdorff distance between the LIDM feature distributions of original tire image and each sliding image patch. Moreover, DFM may be enhanced to improve the robustness of defect detection algorithm by a background suppression. Finally, an effective defect detection algorithm is proposed to achieve the pixel-level detection of defects with high precision over the enhanced DFM. In addition, the defect detection algorithm is not only robust to the noise in the background, but also has a more powerful capability of handling different shapes of defects. To validate the performance of our proposed method, two kinds of experiments about the defect feature map and defect detection are conducted to demonstrate its good performance. Moreover, a series of comparative analyses demonstrate that the proposed algorithm can accurately detect the defects and outperforms other algorithms in terms of various quantitative metrics.

Adaptive Feature Selection and Feature Fusion for Semi-supervised Classification

Labeling of data is often difficult, expensive, and time consuming since efforts of experienced human annotators are required, and often we have large number of samples and noisy data. Co-training is a practical and powerful semi-supervised learning method as it yields high classification accuracy with a training data set containing only a small set of labeled data. For successful co-training performance, two important conditions need to be satisfied for the features: diversity and sufficiency. In this paper, we propose a novel mutual information based approach inspired by the idea of dependent component analysis to achieve feature splits that are maximally independent between-subsets (diverse) or within-subsets (sufficient). In addition, we demonstrate the application of the method to a real world problem, classification of laser tread mapping tire data. We introduce several features that are designed to highlight physical characteristics of the tire data, as well as local or global descriptors, such as histograms, gradients, or representations in other domains. Results from both simulations and tire image classification confirm that co-training with the proposed feature set and feature splits consistently yields higher accuracy than supervised classification, when using only a small set of labeled training data is available. The proposed method presents a very promising complement to time consuming and subjective expert labeling of data, reducing expert efforts to a minimum. Further results show that by using a probabilistic multi-layer perceptron classifier as the base learner in co-training, our method leads to very meaningful continuous measures for the progression of irregular wear on tire surface.

Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.