In order to improve the safety of the remote laser wireless charging system used in smart homes, a laser wireless charging protection system based on deep learning was adopted. Meanwhile, in response to the small target of photovoltaic cells attached to the surface of smart homes, which were difficult to identify, a YOLOv7-NH network model was improved to establish a protection monitoring area and incorporate inter frame difference method for real-time monitoring of charging areas. A protection algorithm for image monitoring of the area where the charging target was located was written through the steps of creating a principle analysis algorithm framework building environment debugging, and a testing system was built. The test results show that when the distance between the laser emitting end and the charging target is within 10 m, the response start time of the protection system built based on this algorithm is less than 1ms. That is, when a moving foreign object enters the protection monitoring area with a size of 40 mm×40 mm at a speed of 1.5 m/s below the normal speed, the protection system can stop laser emission before it moves to the optical path where the laser beam is located. This result is helpful for the development of indoor laser remote wireless charging protection technology.