The use of wood, such as Cross Laminated Timber (CLT), has increased over the last years in the construction of buildings. CLT is primarily considered a structural component inside walls, floors, and ceilings. But, when the design specifies an exposed surface of the CLT, where the wood surface will be visible in the finished building, there are requirements on the visual appearance of the CLT. To fulfil the requirements visual inspections of wood defects of the surface are conducted, before the CLT-panel is shipped to the construction site. Human inspectors are not consistent evaluators of products, and their accuracy depends on factors such as expertise, motivation, and fatigue. This study proposes a solution to automate the inspection, where captured images are analysed by an object detection model, based on deep learning. The model Faster R-CNN has proven to be successful in medical image processing and for object detection. A challenge with deep learning models, such as Faster R-CNN, is the number of test images needed to train the model. Also, there are a limited number of suitable test images of wood defects available. By using a pre-trained Faster R-CNN model and adapt the model's skill to detect wood defects, instead of the model's intended objects, the model can be trained with a relatively small number of test images. This study relies on images of planks, similar to the ones used in CLT, and the number of test images in the study is very limited. To improve the results, the model needs to be trained with more test images of CLT-panels. Nevertheless, the model can locate the defects in the CLT-panel and identify the type of defect to a high degree. The results indicate that the proposed model can be used to automate the inspection of wood defects on the CLT surface.