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Aiming at the problem of high cost and high risk of manual inspection of cages in marine aquaculture, a new method of unmanned cage inspection is proposed based on the application background of monitoring of cages in marine aquaculture. This method can efficiently complete the data collection of basic aquaculture by using UAV overlooking observation method and combining with the information of daily cage culture conditions. On the basis of video data captured by UAV, key frames are extracted, and image preprocessing is carried out by using the Holistically-nested edge detection network (HED), which reduces the redundant information of the image and obtains the edge of the cage. On this basis, the edge image is binarized and extracted the target region , and the corresponding adaptive threshold selection rules are proposed. At last, according to the improved Tamura texture features, the validity of the data in the cage farming area is judged. This method combines depth learning method with traditional image detection technology, and has strong environmental adaptability and high accuracy. Finally, the aquaculture cage of Dali Red Fin Oriental Fugu Farms in Dalian Tianzheng Industrial Co., Ltd. was taken as the experimental object. The accuracy of extracting the aquaculture cage of puffer dolphin at sea was 97%, and the accuracy of judging the validity of the information was 97.1%.