A real time detection method for abnormal strapping of steel coil based on CCD active imaging

In order to accurately detect the abnormal looseness of strapping in the process of steel coil hoisting, an accurate detection method of strapping abnormality based on CCD structured light active imaging is proposed. Firstly, a maximum entropy laser stripe automatic segmentation model integrating multi-scale saliency features is constructed. With the help of saliency detection model, the purpose is to reduce the interference of the environment to the laser stripe and highlight the distinguishability between the stripe and the background. Then, the maximum entropy is used to segment the fused saliency features and accurately extract the stripe contour. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, the stripe center line is extracted based on the stripe distribution normal direction, and the abnormal strapping is recognized online according to the stripe center. Experiments show that the proposed method is effective in terms of detection accuracy and time efficiency, and has certain engineering application value.

A Novel Method for Laser Peak Detection with Subpixel Accuracy for the Rail Corrugation Measurement

In this article, a new wavelet-based laser peak detection algorithm is proposed having subpixel accuracy. The algorithm provides an accurate and rapid measurement platform for the rail surface corrugation with no need to any image noise elimination. The proposed rail Corrugation Measurement System (CMS) is based on the laser triangulation principle, and the accuracy of such system is mainly affected by the laser peak detection in the captured image. The intensity of each row or column of the image is taken as a 1-D discrete signal. Intensity distribution of a laser stripe in this signal follows a Gaussian pattern contaminated by the white noise. Against usual peak detection algorithms with need to prenoise-filtering process, the proposed method based on the wavelet transform is able to perform these tasks efficiently and robustly. Present wavelet-based methods for the peak detection are at pixel level, but for achieving high accuracy subpixel detection is proposed. Experiments show that the capability of the proposed method for laser peak detection is more accurate and faster than the filter-based methods, especially for low S/N ratios. Also, this technique can be utilized for any application in laser peak detection with subpixel accuracy. A prototype system based on the proposed method for the rail corrugation measurement has been designed and manufactured. Results of the rail corrugation measurement guarantee capability of the proposed methodology for accurate measurement of the rail corrugation and its potential for industrial application.

Improving Sub-pixel Estimation of Laser Stripe Reflection Center by Autoconvolution on FPGA

Modern laser scanners perform high-speed real-time image processing algorithms while operating in harsh industrial environments. Their performance goal is to extract the central position of the laser line reflection with Gaussian distribution. Traditional algorithms for sub-pixel estimation, such as the Center of Gravity (CG) or Parabolic Fit (PF), show poor performances under low SNR or if the pixels are saturated. Data pre-processing usually has a key role in suppressing the effects of various noise sources and dynamic environment, especially when the images are overexposed and the top of Gaussian pulse is flattened. Both in simulation and in experiment, this study explains a method that improves the accuracy of estimation of the laser stripe reflection center, by using an autoconvolution for extending the bit-width of pixel intensity. Autoconvolution of the image line is an efficient real-time pre-processing filtering method for improving the accuracy of CG calculation. The proposed algorithm is implemented on Field-Programmable Gate Arrays (FPGAs) and experimentally validated at real operational environment. It is shown that this method can reduce the error of CG laser reflection center estimation for more than one pixel in size when the image is highly affected by external noise sources and ambient light.

Automated Recognition of Weld Pool Characteristics from Active Vision Sensing

While penetration occurs underneath the workpiece, the raw information used to detect it during welding must be measurable to a sensor attached to the torch. Challenges are apparent because it is difficult to find such measurable raw information that fundamentally correlates with the phenomena occurring underneath. Additional challenges arise because the welding process is extremely complex such that analytically correlating any raw information to the underneath phenomena is practically impossible; therefore, handcrafted methods to propose features from raw information are human dependent and labor extensive. In this paper, the profile of the weld pool surface was proposed as the raw information. An innovative method was proposed to acquire it by projecting a single laser stripe on the weld pool surface transversely and intercepting its reflection from the mirror-like weld pool surface. To minimize human intervention, which can affect success, a deep-learning-based method was proposed to automatically recognize features from the single-stripe active vision images by fitting a convolutional neural network (CNN). To train the CNN, spot gas tungsten arc welding experiments were designed and conducted to collect the active vision images in pairs with their actual penetration states measured by a camera that views the backside surface of the workpiece. The CNN architecture was optimized by trying different hyperparameters, including kernel number, kernel size, and node number. The accuracy of the optimized model is about 98% and the cycle time in the personal computer is ~ 0.1 s, which fully meets the required engineering application.

Robust Analysis and Laser Stripe Center Extraction for Rail Images

In this paper, a novel method for the effective extraction of the light stripes in rail images is proposed. First, a preprocessing procedure that includes self-adaptive threshold segmentation and brightness enhancement is adopted to improve the quality of the rail image. Secondly, center of mass is utilized to detect the center point of each row of the image. Then, to speed up the procedure of centerline optimization, the detected center-points are segmented into several parts based on the geometry of the rail profile. Finally, piecewise fitting is adopted to obtain a smooth and robust centerline. The performance of this method is analyzed in detail, and experimental results show that the proposed method works well for rail images.