In the detection of bamboo surface defects, bamboo surface defects have different shapes and the imaging environment is messy. The existing target detection methods based on convolutional neural networks(CNNs) have low detection accuracy when facing such specific data. Moreover, the source of bamboo chips is complex and there are other restrictions, such as different colors in different seasons, so it is impossible to collect all kinds of data, resulting in a small amount of data on bamboo chip surface defects, so that the CNN can not fully learn. In view of the above problems, this paper proposes an improved bamboo defect identification method combining the ResNet50 network and transfer learning. Firstly, the obtained positive and negative sample data are divided into a training set, a verification set and a test set according to a certain proportion. Secondly, the OTSU algorithm and LBP algorithm are used to binarize the bamboo image and extract its features to reduce the influence of noise. Finally, the ResNet50 is used as the backbone network, and $ L_2 $ regularization, label smoothing and migration learning are combined to obtain an optimized model suitable for bamboo defect detection and recognition. The proposed detection networks, VGG16, DenseNet121, ResNet50 and YOLOv3, which are commonly used in industrial detection at present, are trained and tested on the same scale training test set respectively. The experimental results show that the average precision mAP of the proposed detection networks is 23.45, 18.6, 19.51 and 2.76 percentage points higher than that of VGG16, DenseNet121, YOLOv3 and ResNet50, respectively. The proposed method can effectively detect the surface defects of bamboo chips with different shapes, and reduce the time consumption, which has a good effect in practical industrial application.