Image-Based Surface Defect Detection Using Deep Learning: A Review

Surface detection of small defects plays a vital role in manufacturing and has attracted broad interest. It remains challenging primarily due to the small size of the defect relative to the large surface and the rare occurrence of defects. To address this problem, in this article we propose a novel machine vision approach for automatically identifying the tiny flaws that may appear in a single image. First, the presented defect exaggeration approach produces both the flawless image and the corresponding exaggerated version of the defect by taking the variations in the image as regularization terms. Second, a generative adversarial network (GAN) in conjunction with a convolutional neural network (CNN) is proposed to guarantee the accuracy of tiny surface defect detection by producing exaggerated defect image samples. Furthermore, the limited dataset of the training samples for defect detection is enlarged by exploiting the GAN technique with the variation exaggerated images. To evaluate the performance of our proposed method, we conduct comparison experiments between the state-of-the-art techniques with and without the proposed algorithm as well as comparison experiments between the state-of-the-art techniques and our method. The experimental results on different types of surface image samples demonstrate that the proposed method can significantly improve the performance of the state-of-the-art approaches while achieving a defect detection accuracy of 99.2%.

Deep-Learning-Based Small Surface Defect Detection via an Exaggerated Local Variation-Based Generative Adversarial Network

One class based feature learning approach for defect detection using deep autoencoders

Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing.

/pdf/defect-detection-methods-for-industrial-products-using-deep-iis5e8k1.pdf

Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

Localization is critical for automated vehicles as it provides essential position, velocity, and heading angle information to perform object tracking, trajectory prediction, motion planning, and control. However, model/environmental uncertainties (including road friction) and noises in sensor measurements have a significant effect on the accuracy of the localization and vehicle state estimation, specially for perceptually degraded conditions. In this article, an integrated localization method based on the fusion of an inertial dead reckoning model and 3D LiDAR-based map matching is proposed and experimentally verified in an urban environment with varying environmental conditions. Leveraging a global navigation satellite system (GNSS), inertial navigation system (INS), and 3D LiDAR point clouds, a novel light point cloud map generation method, which only keeps the necessary point clouds (i.e., buildings and roads regardless of vegetation varying with seasonal change), is proposed. Subsequently, based on the onboard sensors and the pre-built light point cloud map, a dead reckoning model is derived and integrated with the normal distribution transformation (NDT) based map matching algorithm by an error-state-constrained Kalman filter to limit the localization error. On top of the Kalman filter, the stability analysis of the estimator is presented. Finally, the performance of the algorithm is validated in real road experiments under various environmental conditions. Thorough experiments in winter and summer and associated results confirm the advantages of integrating the proposed light point cloud map generation with the dead reckoning model in terms of accuracy and reduced computational complexity.

Integrated Inertial-LiDAR-Based Map Matching Localization for Varying Environments

Surface defect detection is a common task in industry production. Generally, designer has to find out a suitable feature to separate defects in the image. The hand-designed feature always changes with different surface properties which lead to weak ability in other datasets. In this paper, we firstly present a general detecting method based on convolutional neural network (CNN) to overcome the common shortcoming. CNN is used to complete image patch classification. And features are automatically exacted in this part. Then, we build a voting mechanism to do a final classification and location. The good performances obtained in both arbitrary textured images and special structure images prove that our algorithm is better than traditional case-by-case detection one. Subsequently, we accelerate algorithm in order to achieve real-time requirements. Finally, multiple scale detection is proposed to get a more detailed locating boundary and a higher accuracy.

A Surface Defect Detection Based on Convolutional Neural Network

This letter is the first report from a series of IEEE TIV's Decentralized and Hybrid Workshops (DHWs) on Intelligent Vehicles for Education (IV4E). The role of intelligent vehicles in promoting education for all ages through autonomous racing was discussed during a recent DHW. Over the past decade, autonomous racing has emerged due to advancements in self-driving technologies. While still focused on extreme speed, autonomous racing differs from conventional automobile racing in its development philosophy, as human drivers are no longer involved. The absence of human drivers should be regarded as a new chance to increase competitiveness and entertainment value. This letter discusses opportunities to promote education-oriented autonomous racing. Recall that the flagship car race is Formula 1, where “formula” denotes technical restrictions that should be satisfied strictly. We name the new race series Autonomous 1 or A1, leveraging the power of autonomous intelligence in education. The achievements made in Formula 1 and typical autonomous races are reviewed, followed by discussions about A1’s future perspectives. Specifically, A1 needs to maintain race consistency, update rules, and provide personalized commentary to support all-age education.

From Formula One to Autonomous One: History, Achievements, and Future Perspectives

The intersections are junctions of roads. Recognizing intersections based on LiDAR accurately and quickly is a key task for UGVs. In this paper, we propose an end-to-end real-time intersections recognition network (IRNet) with graph attention convolution based on graph classification and 3D LiDAR point cloud. Multiple evaluations on KITTI and Tunnel dataset demonstrate that our model performs better than other competitive models and meets the real-time recognition. We research the performance of our model under different number of input points, and certify that neither of two spatial transformation networks is effective for our model. Ablation experiments certify effectiveness of the proposed features skip connection.

IRNet: A Real-time Intersection Recognition Network Based on Graph Classification and 3D LiDAR Point Cloud for UGVs

As a pixel-level prediction task in computer vision, semantic segmentation relies on sufficient and relatively fine image annotation for its excellent performance. This paper proposes a more flexible two-stage semantic segmentation framework for domain adaptation from virtual scenes to real street scenes. In the first stage, supervised pixel-level contrastive learning is performed using virtual scenes and their semantic labels, enabling the model to learn a highly structured pixel-level feature space. In the second stage, the pixel discriminant model obtained in the first stage is used for semantic prediction of the real scene to achieve the adaptation of the target domain. In this stage, the prediction results of the real scene are used as pseudo-labels to perform unsupervised segmentation of single-frame images in an iterative training manner. In particular, according to the sequence characteristics of street view images, this method iteratively trains and predicts consecutive frames of images, enabling the model to be fine-tuned and iterated as the environment changes. By exploring the above framework, we use semantic segmentation labels easily obtained in virtual scenes to carry out scene adaption. More importantly, knowledge mining in source domain and adaptive adjustment in target domain can be flexibly independent. We apply this method to environment perception for autonomous driving. In sequential street scenes, dynamic iterative prediction is performed on consecutive frames. This approach enhances the adaptability to various target domains. Finally we achieve more competitive results in the domain adaptation experiments of GTA->Cityscapes and GTA->ApolloScape.

A Two-Stage Domain Adaptation Approach Based on Contrastive Learning and Unsupervised Semantic Segmentation

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Papers

A Surface Defect Detection Based on Convolutional Neural Network

From Formula One to Autonomous One: History, Achievements, and Future Perspectives

IRNet: A Real-time Intersection Recognition Network Based on Graph Classification and 3D LiDAR Point Cloud for UGVs

A Two-Stage Domain Adaptation Approach Based on Contrastive Learning and Unsupervised Semantic Segmentation