Mr. Jian Qin Profile

Jian Qin

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Proceedings Article | 29 April 2023 Paper

Image matching algorithm in outdoor environment

Ziyan Luo, Jian Qin, Long Yan

Proceedings Volume 12610, 126101N (2023) https://doi.org/10.1117/12.2671319

KEYWORDS: Feature extraction, Detection and tracking algorithms, Light sources and illumination, Tunable filters, Reflection, Image registration, Reconstruction algorithms, Matrices, Windows, Histograms

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In order to solve the problem that the traditional feature matching algorithm has less premise number of feature points and poor matching ability under outdoor complex lighting conditions, an image matching algorithm based on color invariants in outdoor environment is proposed. Firstly, a feature matching algorithm with color invariants and Tanimoto similarity is designed based on Kubelka Munk theory. By introducing color invariants to distinguish the available feature areas in outdoor scenes, AKAZE (Accelerated KAZE) algorithm and SIFT (Scale invariant Feature Transform) algorithm are combined to generate more comprehensive feature descriptors; Then, Tanimoto similarity test is used to screen feature point pairs and random sample consensus algorithm is used to remove external points. According to the experimental results, under the same conditions, the improved algorithm obtains more effective feature points at the edge of the image and in the smooth area of the image. The average accuracy of the algorithm in outdoor environments reaches 90%, and the number of feature matching is 43% higher than that without color invariants.

Proceedings Article | 3 February 2023 Paper

A lightweight residual neural network for apple external quality grading

Xiaojie Sun, Jian Qin, Long Yan, Xiaojia Liu

Proceedings Volume 12511, 1251138 (2023) https://doi.org/10.1117/12.2660018

KEYWORDS: Convolution, Performance modeling, Neural networks, Instrument modeling, Data modeling, Mobile devices, Feature extraction, Visual process modeling, Machine learning, Process modeling

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The current problem of relatively backward and inefficient apple fruit grading technology, computer vision-based classification methods are widely adopted, but traditional visual classification networks face the problems of many parameters, high computational effort and unsatisfactory classification accuracy. Therefore, this paper proposes a lightweight residual network-based apple external quality grading method. Firstly, based on the traditional residual neural network, the network uses group convolution to replace the standard convolution in the original residual units， the aims are to reduce the number of model parameters and computational effort; Secondly, to address the information non-circulation problem between group channels caused by group convolution, a Channel Shuffle operation is used to mix inter-group features to improve model performance; Finally, a parallel pooling structure is proposed to solve the problem of information loss of traditional pooling features. To build a dataset of apple images with extensive coverage of external quality information, and to perform data enhancement on a limited dataset, and to conduct experiments based on the augmented dataset using improved models in comparison with common neural network models. The experimental results show that the improved lightweight residual network model has only 2.97M parameters, the FLOPs are only 1/5 of the traditional model, and the classification accuracy is 96.5%, which is helpful for future implementation of apple grading in low performance mobile terminals.

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