We propose a new scheme for recognizing the topological charge (TC) of orbital angular momentum (OAM) beams using convolutional neural networks (CNN) based on the focusing of cylindrical lenses and the detection of linear photodiode arrays (PDAs). Simulations and experiments are conducted. For the superimposed OAM sets with different TC values and different TC intervals, the effects of atmospheric turbulence disturbances on recognition accuracy are explored separately, where the turbulence disturbances to the superimposed OAM beams are measured by the coherence length r0. The simulation results show that the recognition accuracy decreases as the turbulence disturbances increase. With 16-unit PDAs, the TC of the superimposed OAM set l∈{±1, ±2, ±3, ±4} can be recognized with 100% accuracy under weak (the coherence length r0 =16.16 cm ) and intermediate (the coherence length r0 =10.66 cm ) turbulence disturbances, and above 90% accuracy under a strong (the coherence length 0 r = 4.06 cm ) turbulence disturbance. In the experiment under weak (the coherence length r0 =13.01 cm) and intermediate (the coherence length r0 = 8.59 cm) laboratory-simulated turbulence disturbances, with 16-unit PDAs, the recognition accuracy reaches 100% and 99.65%, respectively. The experimental results verify the results of the simulation.
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