Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization
Particleboard is an important forest product that can be reprocessed using wood processing by-products. This approach has the potential to achieve significant conservation of forest resources and contribute to the protection of forest ecology. Most current detection models require a significant numb...
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MDPI AG
2025-04-01
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| author | Haifei Xia Haiyan Zhou Mingao Zhang Qingyi Zhang Chenlong Fan Yutu Yang Shuang Xi Ying Liu |
| author_facet | Haifei Xia Haiyan Zhou Mingao Zhang Qingyi Zhang Chenlong Fan Yutu Yang Shuang Xi Ying Liu |
| author_sort | Haifei Xia |
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| description | Particleboard is an important forest product that can be reprocessed using wood processing by-products. This approach has the potential to achieve significant conservation of forest resources and contribute to the protection of forest ecology. Most current detection models require a significant number of tagged samples for training. However, with the advancement of industrial technology, the prevalence of surface defects in particleboard is decreasing, making the acquisition of sample data difficult and significantly limiting the effectiveness of model training. Deep reinforcement learning-based detection methods have been shown to exhibit strong generalization ability and sample utilization efficiency when the number of samples is limited. This paper focuses on the potential application of deep reinforcement learning in particleboard defect detection and proposes a novel detection method, PPOBoardNet, for the identification of five typical defects: dust spot, glue spot, scratch, sand leak and indentation. The proposed method is based on the proximal policy optimization (PPO) algorithm of the Actor-Critic framework, and defect detection is achieved by performing a series of scaling and translation operations on the mask. The method integrates the variable action space and the composite reward function and achieves the balanced optimization of different types of defect detection performance by adjusting the scaling and translation amplitude of the detection region. In addition, this paper proposes a state characterization strategy of multi-scale feature fusion, which integrates global features, local features and historical action sequences of the defect image and provides reliable guidance for action selection. On the particleboard defect dataset with limited images, PPOBoardNet achieves a mean average precision (mAP) of 79.0%, representing a 5.3% performance improvement over the YOLO series of optimal detection models. This result provides a novel technical approach to the challenge of defect detection with limited samples in the particleboard domain, with significant practical application value. |
| format | Article |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-e6be703710fa4baa89cf2af0e83fb7ce2025-08-20T02:25:04ZengMDPI AGSensors1424-82202025-04-01258254110.3390/s25082541Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy OptimizationHaifei Xia0Haiyan Zhou1Mingao Zhang2Qingyi Zhang3Chenlong Fan4Yutu Yang5Shuang Xi6Ying Liu7Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaJiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaJiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaJiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaJiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaJiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaJiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaParticleboard is an important forest product that can be reprocessed using wood processing by-products. This approach has the potential to achieve significant conservation of forest resources and contribute to the protection of forest ecology. Most current detection models require a significant number of tagged samples for training. However, with the advancement of industrial technology, the prevalence of surface defects in particleboard is decreasing, making the acquisition of sample data difficult and significantly limiting the effectiveness of model training. Deep reinforcement learning-based detection methods have been shown to exhibit strong generalization ability and sample utilization efficiency when the number of samples is limited. This paper focuses on the potential application of deep reinforcement learning in particleboard defect detection and proposes a novel detection method, PPOBoardNet, for the identification of five typical defects: dust spot, glue spot, scratch, sand leak and indentation. The proposed method is based on the proximal policy optimization (PPO) algorithm of the Actor-Critic framework, and defect detection is achieved by performing a series of scaling and translation operations on the mask. The method integrates the variable action space and the composite reward function and achieves the balanced optimization of different types of defect detection performance by adjusting the scaling and translation amplitude of the detection region. In addition, this paper proposes a state characterization strategy of multi-scale feature fusion, which integrates global features, local features and historical action sequences of the defect image and provides reliable guidance for action selection. On the particleboard defect dataset with limited images, PPOBoardNet achieves a mean average precision (mAP) of 79.0%, representing a 5.3% performance improvement over the YOLO series of optimal detection models. This result provides a novel technical approach to the challenge of defect detection with limited samples in the particleboard domain, with significant practical application value.https://www.mdpi.com/1424-8220/25/8/2541reinforcement learningparticleboarddeep learningdefect detection |
| spellingShingle | Haifei Xia Haiyan Zhou Mingao Zhang Qingyi Zhang Chenlong Fan Yutu Yang Shuang Xi Ying Liu Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization Sensors reinforcement learning particleboard deep learning defect detection |
| title | Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization |
| title_full | Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization |
| title_fullStr | Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization |
| title_full_unstemmed | Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization |
| title_short | Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization |
| title_sort | surface defect detection for small samples of particleboard based on improved proximal policy optimization |
| topic | reinforcement learning particleboard deep learning defect detection |
| url | https://www.mdpi.com/1424-8220/25/8/2541 |
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