Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of defects in 2,412 1080 × 1440 pixels images. Faster R-CNN was modified, trained, verified and tested based on this database. The recognition rate for this proposed method was excellent. The proposed method was compared with the popular R-CNN and YOLOv3 methods showing simpler, faster, and more accurate defect detection, which demonstrates the superiority of the improved Faster R-CNN for wheel hub defects.
Fault inspection is a key part of ensuring safe operation of freight trains. The development of machine vision technology has resulted in vision-based fault inspection becoming the principal means of fault inspection. An angle cock is an important component in the brake system, and a fault in it could lead to a serious accident. In this paper, we propose an automated vision method to inspect for missing handles on an angle cock during operation of a freight train. Images of the angle cock are acquired and they are analyzed using a proposed gradient encoding histogram and support vector machine that combine to create a detection system. Experimental results show that we achieved a fault detection rate of 99.8% using the proposed system, which represents a good real-time performance and high detection accuracy.
Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester. It is affected by operating parameters of a combine such as feeding rate, the peripheral speed of the threshing cylinder and concave clearance, and shows complex non-linear law. Real-time acquisition of the breakage rate is an effective way to find the correlation of them. In addition, real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard. In this study, a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed. The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process. According to the working characteristics of the combine, the illumination and installation of the light source were optimized, and the lateral lighting system was constructed. A two-step method of "color training-verification" was applied to identify the whole and broken kernels. In the first step, the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation, extract the color spectrum of each particle in color-space HSL and output the recognition model file. The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory. The experiments of about 2300 particles showed that the recognition accuracy of 96% was attained, and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency. The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.
Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key (BSK) is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent inspection process is divided into three stages: first, the region-of-interest (ROI) is segmented from the source image by an improved spatial pyramid matching scheme based on multi-scale census transform (MSCT). To localize the BSK in the ROI, census transform (CT) on gradient images is developed in the second stage. Then gradient encoding histogram (GEH) features and linear support vector machines (SVMs) are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.
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