Real-time detection method of gear contact fatigue pitting based on machine vision.

This paper proposes a real-time detection method for gear contact fatigue pitting based on machine vision in order to improve the detection accuracy and detection efficiency of specimen fatigue pitting in gear contact fatigue tests and to realize the visualization, quantification, and real-time detection of gear pitting. Under the principle of gear meshing and the shooting principle of a line-scan camera, a test detection system for gear contact fatigue is established, and the optimal centrifugal shooting distance for the gear tooth surface is obtained by analyzing the gear rotation process. In response to the phenomenon of image overlap caused by the inconsistency between the speed of each point on the gear tooth profile and the line frequency set by the camera, an image correction algorithm of the gear meshing surface has been proposed, which has been proven to have improved the accuracy of the detection results of gear contact fatigue pitting corrosion. The detection accuracy of fatigue pitting corrosion is improved by combining the preliminary detection and the accurate detection of the fatigue features. The depth information of the extracted contour pitting pits is extracted by the sequential forward selection (SFS) algorithm. The experimental results showed that 0.1216mm2 is the average absolute error of pitting corrosion detection, the average relative error is 2.2188%, and the detection accuracy is 97.7812%. The proposed pitting corrosion detection system advances in visualization, quantification, real-time monitoring, and failure judgment with a new, to the best of our knowledge, experimental approach for gear contact fatigue pitting corrosion detection.

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning.

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

[Virus resistance in transgenic watermelon plants containing a WMV-2 coat protein gene].

Virus disease is a major cause that affects the quality and output of watermelon which is an important fruit in summer. So it is really urgent to develop disease resistance plants. But it takes a long time to breed such plants in conventional ways, and it is very difficult to get ideal result. With the development of plant genetic engineering, new ways have been found to breed plants with disease resistance. By using plant transgenic technique, much progress was been made in plant improvement. There are many successful cases of transgenic plants against corresponding virus disease through transferring coat protein gene. This paper reports the results of inheritance, segregation, expression of WMV-2 coat protein gene in inbred transgenic watermelon and its resistance to virus. Through PCR analysis of inbred plants, we found WMV-2 coat protein gene in the genome of progeny R1 separated with 3:1. After successive selection and identification of 4 generations, 8 transgenic pure lines with almost the same agronomic traits were obtained from 3 independent transformants of T7, T11 and T32. The result of Western blotting shows all 3 different transgenic lines of R4T7-1, R4T11-3 and R4T32-7 can produce coat protein. Disease resistance experiment on transgenic plants with WMV-2 shows that, compared with the control groups, transgenic plants can delay the disease infection and reduce the incidence and the symptoms of virus disease. And the transgenic line R4T32-7 expressed high resistance to infection by WMV-2, which lays a foundation for breeding of disease resistant varieties through plant transgenic technique.