Research on Target Defect Detection Based on Machine Vision

Abstract

The daily detection of highspeed electric multiple units (EMU) body is very important for China railway maintenance system. This paper proposes a new method based on machine vision to detect bolts and switches on EMU side skirt, which aims to replace manpower. Yolov3 network is used to identify and locate bolts and switches. After positioning, the status of bolts is detected through Alexnet network. Experiments show that the processing method can achieve the detection work efficiently and accurately. It is suitable for many cases such as insufficient illumination and non-vertical shooting of image samples, which has high robustness.

Maize seed appearance quality assessment based on improved Inception-ResNet

“…Appearance quality is an important factor affecting the price of corn seeds, and effective identification of seed quality is critical for ensuring food security and agricultural production safety. With the rapid advancements in automation, machine vision technology ( Huang et al, 2019 ; Kim et al, 2020 ; Wang and Xiao, 2020 ; Ansari et al, 2021 ; Lu et al, 2022 ) can be used to nondestructively and quickly obtain seed surface feature information at a low cost and high detection accuracy and efficiency, thereby providing potential new methods for seed quality identification.…”

Research on Industrial Production Defect Detection Method Based on Machine Vision Technology in Industrial Internet of Things

“…If all links of incoming materials, processing, assembly, packaging and logistics are highly automated, the equipment on the production line needs accurate operation, quick response and close networking cooperation, where the functional assistance of machine vision technology plays a key role [9][10][11][12][13]. Machine vision can reliably identify various targets in the production process, and is helpful for the control system to make accurate and rapid production decisions [14][15][16][17][18][19][20][21]. Different from the recognition and detection of some known features, it is difficult to realize defect detection in machine vision applications.…”