Machine-vision-based surface defect inspection is one of the key technologies to realize intelligent manufacturing. This paper provides a systematic review on leather surface defect inspections based on machine vision. Leather products are regarded as the most traded products all over the world. Automatic detection, location, and recognition of leather surface defects are very important for the intelligent manufacturing of leather products, and are challenging but noteworthy tasks. This work investigates a large amount of literature related to leather surface defect inspection. In addition, we also investigate and evaluate the performance of some edge detectors and threshold detectors for leather defect detection, and the identification accuracy of the classical machine learning method SVM for leather surface defect identification. A detailed and methodical review of leather surface defect inspection with image analysis and machine learning is presented. Main challenges and future development trends are discussed for leather surface defect inspection, which can be used as a source of guidelines for designing and developing new solutions in this field.
Most of the papers have explored the interactions between solitons with a zero background, while reports about exact solutions for nonzero background are rare. Hence, this paper is aimed at exploring the breather, lump, and interaction solutions with a small perturbation to ( 2 + 1 )-dimensional generalized Kadomtsev-Petviashvili (gKP) equation. General high-order periodic breather solutions are obtained using Hirota’s bilinear method with a small perturbation. At the same time, combining the use of long wave limit methods and module resonance constraints, general lump solutions and mixed solutions to gKP equation are generated. Finally, the space-time structures of the breather solutions, lump solutions, and interaction solutions are investigated and discussed.
Automatic license plate recognition system (ALPR) has been widely used in intelligent transportation and other fields. However, in complex environments such as sound source vehicle location, low light, or bad weather conditions, ALPR is still a challenging problem. Aiming at the problem, a deep learning framework is developed based on depthwise over-parameterized convolution recurrent neural network for license plate character recognition. The proposed framework is composed as follows: i) License plate correcting module based on Spatial Transformation Network; ii) Feature extraction module based on Depthwise Over-parameterized Convolution; iii) Sequence annotation module based on Bidirectional Long Short-Term Memory; iv) Regularized sequence decoding module based on Connectionist Temporal Classification with maximum conditional entropy. Two open-source datasets in China are used to verify the performance of the algorithm. The proposed framework can effectively correct distorted and inclined license plates in space, and recognize the license plate end-to-end, which avoids the complex character segmentation process. Compared with some current state-of-art algorithms, the proposed algorithm has higher recognition accuracy and better robustness.
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