Xuefei Chen1, Wenhui Tan2, Qiulan Wu1,*, Feng Zhang1, Xiumei Guo1, Zixin Zhu1
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3143-3157, 2023, DOI:10.32604/iasc.2023.040903
Abstract In order to improve the accuracy and efficiency of Lentinula edodes logs contamination identification, an improved YOLOv5s contamination identification model for Lentinula edodes logs (YOLOv5s-CGGS) is proposed in this paper. Firstly, a CA (coordinate attention) mechanism is introduced in the feature extraction network of YOLOv5s to improve the identifiability of Lentinula edodes logs contamination and the accuracy of target localization. Then, the CIoU (Complete-IOU) loss function is replaced by an SIoU (SCYLLA-IoU) loss function to improve the model’s convergence speed and inference accuracy. Finally, the GSConv and GhostConv modules are used to improve and optimize More >
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