This study presents an automated defect detection system for photovoltaic modules that combines image processing techniques with deep learning models. The system identifies 21 types of defects using three imaging meth...
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This study presents an automated defect detection system for photovoltaic modules that combines image processing techniques with deep learning models. The system identifies 21 types of defects using three imaging methods: infrared imaging, red-green-blue imaging, and electroluminescence imaging. Infrared imaging, captured using a thermal imager mounted on a drone, detects thermal anomalies such as hotspots and open circuits. red-green-blue imaging identifies surface-level defects, including glass breakage, soiling, and vegetation shading. Electroluminescence imaging, obtained with a charge-coupled device camera in a controlled darkroom environment, reveals internal defects such as microcracks, cell degradation, and busbar corrosion. Crossreferencing results from infrared and red-green-blue images facilitates the identification of defect causes, while electroluminescence imaging confirms internal issues and provides targeted recommendations for improvements. The system development was divided into four main stages. First, electroluminescence images were preprocessed and segmented using median filtering, thresholding, edge detection, and perspective correction techniques. Second, color features in infrared and red-green-blue images were enhanced through color space transformation to improve detection accuracy. Third, image datasets were augmented using rotation, flipping, and generative techniques to ensure model reliability. Finally, deep learning models were trained using transfer learning methods and optimized through cross-validation and hyperparameter tuning to achieve optimal performance for each imaging type. The proposed system demonstrates exceptional accuracy: 99.06% for thermal defectdetection in infrared images, 100% for surface defectdetection in red-green-blue images, and 99.2% for internal defectdetection in electroluminescence images. The average processing time per image is less than 0.1 s, making the system suitable for real-time and large-scale p
automated surface defectdetection is essential to manufacturing automation. However, automated inspection of aero-engine blades remains challenging due to tiny defects and weak features. To address this issue, we pro...
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automated surface defectdetection is essential to manufacturing automation. However, automated inspection of aero-engine blades remains challenging due to tiny defects and weak features. To address this issue, we propose a semantic prior guided defect perception network, named SPDP-Net, which is ultimately integrated into an automatedsystem to achieve efficient detection of defects. Firstly, a semantic prior mining module (SPM) is developed to capture finer-grained pixel-level location priors of defects by leveraging image feature mapping relations which facilitates the precise perception of tiny defects. Subsequently, we propose a defect enhancement perception module (DEP) to separate weak defects from complex backgrounds by utilizing defect location priors provided by SPM to enhance the features of defects while suppressing the values of non-defect regions, which makes the weak defects present as more obvious outliers. Finally, the global information extraction module (GIE) extracts the global features of defects, which helps to further improve the predicted results. When equipped with SPM, DEP and GIE, SPDP-Net can accurately identify and locate defects, exhibiting more competitive recognition and feature extraction capabilities for tiny defects and weak defects. To evaluate the effectiveness of our method, we construct an aero-engine blade surface defectdetection dataset from real industrial scenarios called ABSDD with the collaboration of senior engineers. We achieve 95.9% precision, 94.0% recall and 94.9% F1 score on ABSDD. In addition, we also achieve state-of-the-art results on two public benchmark datasets, KSDD2 and DAGM. Finally, we have applied the developed SPDP-Net to an automatedsystem and have conducted actual tests in collaboration with a well-known aero-engine production company. Note to Practitioners-At present, the automateddetectionsystem for surface defects in industrial manufacturing has not been well developed, which is particularly tra
Today the manufacturing world is facing major pressures due to the globalization of markets. Internal and external organizational pressures have led to increased competition, market complexity, and new customer demand...
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ISBN:
(纸本)9781728186092
Today the manufacturing world is facing major pressures due to the globalization of markets. Internal and external organizational pressures have led to increased competition, market complexity, and new customer demands. It has been noted that organizations and companies adopt lean or agile manufacturing strategies to overcome this problem. One of those strategies, the use of artificial intelligence and machine vision in the automotive industry, appeared as a good opportunity created by the continuous development of the computer-integrated manufacturing systems and the digitalization of the industry. In the automotive manufacturing process, developing a new automated defect detection system (ADDS), adapted for the automotive manufacturing requirements and particularities, and improved with artificial intelligence techniques, it is of major importance. In this paper we focus on a specific problem from the automotive industry, the problem of testing engine blocks using the information from real images.
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