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• Four types of defects in rubber timber are performed by improved YOLOX. • Improve the feature fusion module of YOLOX by adding ECA attention mechanism and ASSF multi-feature adaptive fusion. • Improve the confidence loss function and change BCE loss to Focal loss. • The regression of the target box was performed using EIOU loss. Deep learning has achieved certain results in the field of wood surface defect detection. To address the problems of low accuracy of the detection results of surface defects on boards, slow detection speed and large number of model parameters, this article take advantage of computer vision to improve the feature fusion module of YOLOX target detection algorithm, by adding efficient channel attention (ECA) mechanism, adaptive spatial feature fusion mechanism (ASFF) and improve the confidence loss and localization loss functions as Focal loss and Efficient Intersection over Union (EIoU) loss, to enhance the feature extraction ability and detection accuracy of the algorithm. Considering the depth and width of the model, the depth-separable convolution and optional multi-version algorithm are used to reduce the model parameters and computational effort to seek the optimal model. Experiments show that the improved model detects four types of defects in rubber timber with a considerable improvement and has significant advantages over other target detection algorithms. [ABSTRACT FROM AUTHOR]