The PASCAL Visual Object Classes Challenge

Survey on semantic segmentation using deep learning techniques

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Automated Visual Defect Detection for Flat Steel Surface: A Survey

A Brief Survey on Semantic Segmentation with Deep Learning

As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow-learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.

Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

In this correspondence paper, an image dynamics-based visual servoing for quadrotors is proposed to realize stable hovering and tracking. Four perspective image moments are adopted as visual features to control all the independent degrees of freedom of a quadrotor. The complicated interaction matrix is simplified by projecting original image to virtual image plane. On this basis, the dynamics of the system is determined by considering the dynamics of image features and the quadrotor simultaneously. Backstepping controllers are then designed to stabilize the visual servoing system of the quadrotor. In reality, it is unrealistic to have exact prior knowledge about the trajectory parameters of an unpredictable moving target. To solve this problem, a trajectory observer based on nonlinear tracking-differentiator to estimate trajectory parameters of the target is firstly integrated into the quadrotor with image dynamics, which guarantees a satisfactory performance. The effectiveness of the proposed approach is verified by simulations.

Image Dynamics-Based Visual Servoing for Quadrotors Tracking a Target With a Nonlinear Trajectory Observer

A vision-based target-following guider for mobile robot is presented in this paper. It consists of three parts: the visual tracking part; the target redetection part; and the visual servo part. In the visual tracking part, the target contour band is proposed for irregular sampling, which can improve the performance of the correlation filter-based methods. In the target redetection part, potential targets are searched by an offline trained detector. Then an online training module is used to determine the real target and achieve accurate positioning. The interaction matrix of point features is used in the visual servo part for motion control to maintain the relative pose between the target and the robot. The visual tracking part and the target redetection part are tested respectively on many videos, which proved to work well. To show the effectiveness of our method, some state-of-the-art methods are also tested. The target-following guider is evaluated on mobile robots. In our experiments, the robot can robustly follow the human target over a long distance, which strongly proved the validity of our target-following guider.

Xilong Liu

Papers

Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Image Dynamics-Based Visual Servoing for Quadrotors Tracking a Target With a Nonlinear Trajectory Observer

Vision-Based Target-Following Guider for Mobile Robot

Weak scratch detection and defect classification methods for a large-aperture optical element