Solar cells defects inspection plays an important role to ensure the efficiency and lifespan of photovoltaic modules. However, it is still an arduous task because of the diverse attributes of electroluminescence images, such as indiscriminative complex background with extremely unbalanced defects and various types of defects. In order to deal with these problems, this paper proposes a new precise and accurate defect inspection method for photovoltaic electroluminescence (EL) images. The proposed algorithm leverages the advantage of multi attention network to efficiently extract the most important features and neglect the nonessential features during training. Firstly, we designed a channel attention to exploit contextual representations and spatial attention to effectively suppress background noise. Secondly, we incorporate both attention networks into modified U-net architecture and named it multi attention U-net (MAU-net) to extract effective multiscale features for defects inspection. Finally, we propose a hybrid loss which combines focal loss and dice loss aiming to solve two problems: a) overcome the class imbalance problem, and b) allowing the network to train with irregular image labels for some complex defects. The proposed multi attention U-net is evaluated on real photovoltaic EL images datasets using 5-fold cross validation technique. Experimental results demonstrate that the proposed network can segment and detect various complex defects correctly. The proposed method achieved the mean intersection over-union (m-IOU) of 0.699 and F-measure of 0.799 which outperforms the previous methods.

Unsupervised Domain Adaptation (UDA) is a key field in visual recognition, as it enables robust performances across different visual domains. In the deep learning era, the performance of UDA methods has been driven by better losses and by improved network architectures, specifically the addition of auxiliary domain-alignment branches to pre-trained backbones. However, all the neural architectures proposed so far are hand-crafted, which might hinder further progress.

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A decoupled visual model and a fractional PID controller are designed aiming at the problem of the view distortion in the fillet weld welding process. Firstly, the intersection point coordinates of two laser stripes are selected as the image characteristics, and the decoupled visual model is designed to the tracking control of the fillet weld seam so that the control value in two directions is decoupled, reducing the difficulty of control. In addition to this, the image may produce distortion in the dynamic tracking process, causing nonlinearity and coupling in two directions. To solve this problem, the fractional-order PID controller is designed so that the adjustment range and the control ability are improved better than the traditional PID controller. Some experiments verify that the desired performance can be achieved by using the proposed methods.

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