The Detection of Solder Joint Defect and Solar Panel Orientation Based on ELM and Robust Least Square Fitting
This paper investigates the detection methods of solder joint defect and solar panel orientation based on extreme learning machine (ELM) and robust least square fitting (RLSF). The work first adopts image processing techniques to preprocess the images of solder joint and solar panel, then applies ELM to recognize those defected solder joints, and take the RLSF algorithm to acquire the edge of solar panel. Solder joint image features, such as area, gravity, anisotropy, and inertial moment are extracted and input to ELM for defect recognition. Experimental results show that the approaches can get a recognition rate of over 96％. For the solder joints defect detection, we can acquire the accurate edge of solar panel. After solar panel edge is determined, the orientation parameters-position shift, deflection angle and edge lengths of solar panel can be easily obtained.
Defect detection Orientation detection Extreme learning machine Robust least square fitting
Caihong Zhang Heng Liu
Southwest University of Science and Technology, Mianyang, 621010 China
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
561-565
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）