Light Weight Apple Defect Detection by Gaussian Mixture Model and Attention Mechanism

Ma Xiubo, Sun Xiongwei, Shi Shaoqing
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:110-119, 2024.

Abstract

To improve the speed and accuracy of online apple defect detection, a modified PP-PicoDet model based on mixed Gaussian color modeling and spatial attention mechanism is proposed. Firstly, in order to enhance the detection performance of small and low-contrast defects, the method constructs an offline color model based on GMM theory and builds a saliency numerical channel that integrates the saliency of anomalous targets on RGB data. Secondly, the SE mechanism of the network is optimized, integrating the CBAM (convolution block attention module) structure to enhance the perception of structural features in anomalous regions through strengthened spatial attention mechanisms. Furthermore, the abnormal loss function is adjusted by employing a Gaussian model to establish a label assignment loss function strategy, simplifying parameter tuning and improving the model’s motivation for small proportion defects. This enhancement aims to improve the detection performance of the model under conditions of imbalanced samples. Experiments indicate that the algorithm, implemented on a self-built application platform using real-world datasets, achieved a 5.6% increase in accuracy and a 5.8% increase in recall at the cost of only 0.5% time delay. The algorithm meets the requirements of timeliness and reliability for detecting defects online, contributing to the enhancement of efficiency in apple quality grading and production.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-xiubo24a, title = {Light Weight Apple Defect Detection by Gaussian Mixture Model and Attention Mechanism}, author = {Xiubo, Ma and Xiongwei, Sun and Shaoqing, Shi}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {110--119}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/xiubo24a/xiubo24a.pdf}, url = {https://proceedings.mlr.press/v245/xiubo24a.html}, abstract = {To improve the speed and accuracy of online apple defect detection, a modified PP-PicoDet model based on mixed Gaussian color modeling and spatial attention mechanism is proposed. Firstly, in order to enhance the detection performance of small and low-contrast defects, the method constructs an offline color model based on GMM theory and builds a saliency numerical channel that integrates the saliency of anomalous targets on RGB data. Secondly, the SE mechanism of the network is optimized, integrating the CBAM (convolution block attention module) structure to enhance the perception of structural features in anomalous regions through strengthened spatial attention mechanisms. Furthermore, the abnormal loss function is adjusted by employing a Gaussian model to establish a label assignment loss function strategy, simplifying parameter tuning and improving the model’s motivation for small proportion defects. This enhancement aims to improve the detection performance of the model under conditions of imbalanced samples. Experiments indicate that the algorithm, implemented on a self-built application platform using real-world datasets, achieved a 5.6% increase in accuracy and a 5.8% increase in recall at the cost of only 0.5% time delay. The algorithm meets the requirements of timeliness and reliability for detecting defects online, contributing to the enhancement of efficiency in apple quality grading and production. } }
Endnote
%0 Conference Paper %T Light Weight Apple Defect Detection by Gaussian Mixture Model and Attention Mechanism %A Ma Xiubo %A Sun Xiongwei %A Shi Shaoqing %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-xiubo24a %I PMLR %P 110--119 %U https://proceedings.mlr.press/v245/xiubo24a.html %V 245 %X To improve the speed and accuracy of online apple defect detection, a modified PP-PicoDet model based on mixed Gaussian color modeling and spatial attention mechanism is proposed. Firstly, in order to enhance the detection performance of small and low-contrast defects, the method constructs an offline color model based on GMM theory and builds a saliency numerical channel that integrates the saliency of anomalous targets on RGB data. Secondly, the SE mechanism of the network is optimized, integrating the CBAM (convolution block attention module) structure to enhance the perception of structural features in anomalous regions through strengthened spatial attention mechanisms. Furthermore, the abnormal loss function is adjusted by employing a Gaussian model to establish a label assignment loss function strategy, simplifying parameter tuning and improving the model’s motivation for small proportion defects. This enhancement aims to improve the detection performance of the model under conditions of imbalanced samples. Experiments indicate that the algorithm, implemented on a self-built application platform using real-world datasets, achieved a 5.6% increase in accuracy and a 5.8% increase in recall at the cost of only 0.5% time delay. The algorithm meets the requirements of timeliness and reliability for detecting defects online, contributing to the enhancement of efficiency in apple quality grading and production.
APA
Xiubo, M., Xiongwei, S. & Shaoqing, S.. (2024). Light Weight Apple Defect Detection by Gaussian Mixture Model and Attention Mechanism. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:110-119 Available from https://proceedings.mlr.press/v245/xiubo24a.html.

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