Micro-cracks detection of multicrystalline solar cell surface based on self-learning features and low-rank matrix recovery. Issue 3 (8th February 2018)

• Record Type: Journal Article
• Authors: Qian, Xiaoliang; Zhang, Heqing; Yang, Cunxiang; Wu, Yuanyuan; He, Zhendong; Wu, Qing-E; Zhang, Huanlong
• Is Part Of: Sensor review. Volume 38:Issue 3(2018)
• Page Start: 360
• Page End: 368
• Publication Date: 2018-02-08
• Subjects: Low-rank matrix recovery -- Micro-cracks detection -- Morphological processing -- Multicrystalline solar cell -- Self-learning features -- Superpixel segmentation; Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-08-2017-0166
• Languages: English
• ISSNs: 0260-2288

Abstract

Abstract : Purpose: This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy. Design/methodology/approach: A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing. Findings: Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy. Originality/value: First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.