  Lobster eye telescopes are ideal monitors to detect X-ray transients, because
they could observe celestial objects over a wide field of view in X-ray band.
However, images obtained by lobster eye telescopes are modified by their unique
point spread functions, making it hard to design a high efficiency target
detection algorithm. In this paper, we integrate several machine learning
algorithms to build a target detection framework for data obtained by lobster
eye telescopes. Our framework would firstly generate two 2D images with
different pixel scales according to positions of photons on the detector. Then
an algorithm based on morphological operations and two neural networks would be
used to detect candidates of celestial objects with different flux from these
2D images. At last, a random forest algorithm will be used to pick up final
detection results from candidates obtained by previous steps. Tested with
simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe,
our detection framework could achieve over 94% purity and over 90% completeness
for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than
94% purity and moderate completeness for targets with lower flux at acceptable
time cost. The framework proposed in this paper could be used as references for
data processing methods developed for other lobster eye X-ray telescopes.
