Real-time detection of wood defects based on SPP-improved YOLO algorithm

Wood processing is one of the most widely used in agriculture and industry. Low precision and high time delay of machine learning in wood defect detection are currently the main factors restricting the production efficiency and product quality of the wood processing industry. An SPP-improved deep learning method was proposed to detect wood defects based on the basic framework of the YOLO V3 network to improve accuracy and real-time performance. The extended dataset was firstly established by image data enhancement and preprocessing based on the limited samples of the wood defect dataset. Anchor box scale re-clustering of the wood defect dataset was carried out according to the defect features. The spatial pyramid pooling (SPP) network was applied to improve the feature pyramid (FP) network in YOLO V3. The validity and real-time performance of the proposed algorithm were verified by a randomly selected test set. The results show that the overall detection accuracy rate on the wood defect test dataset reaches 93.23% while the detection time for each image is within 13 ms.

Automated summary

Wood processing is widely used in agriculture and industry. However, the low precision and high time delay of machine learning in wood defect detection hinder the production efficiency and product quality of the wood processing industry. To address this, an SPP-improved deep learning method based on the YOLO V3 network was proposed. The method achieved high accuracy and real-time performance by establishing an extended dataset, clustering the wood defect dataset based on defect features, and applying the SPP network to improve the feature pyramid network in YOLO V3. The proposed algorithm was validated using a randomly selected test set, and the results demonstrated an overall detection accuracy rate of 93.23% on the wood defect test dataset with a detection time of 13 ms per image.