In X-ray testing, the aim is to inspect those inner parts of an object that cannot be detected by the naked eye. Typical applications are the detection of targets like blow holes in casting inspection, cracks in welding inspection, and prohibited objects in baggage inspection. A straightforward solution today is the use of object detection methods based on deep learning models. Nevertheless, this strategy is not effective when the number of available X-ray images for training is low. Unfortunately, the databases in X-ray testing are rather limited. To overcome this problem, we propose a strategy for deep learning training that is performed with a low number of target-free X-ray images with superimposition of many simulated targets. The simulation is based on the Beer-Lambert law that allows to superimpose different layers. Using this method it is very simple to generate training data. The proposed method was used to train known object detection models (e.g. YOLO, RetinaNet, EfficientDet and SSD) in casting inspection, welding inspection and baggage inspection. The learned models were tested on real X-ray images. In our experiments, we show that the proposed solution is simple (the implementation of the training can be done with a few lines of code using open source libraries), effective (average precision was 0.91, 0.60 and 0.88 for casting, welding and baggage inspection respectively), and fast (training was done in a couple of hours, and testing can be performed in 11ms per image). We believe that this strategy makes a contribution to the implementation of practical solutions to the problem of target detection in X-ray testing.