Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster

River sand bodies have complex and changeable characteristics and distribution. In order to improve the accuracy and efficiency of target recognition, this study proposes a target recognition method of ultra-deep river sand bodies with improved deep learning under unmanned aerial vehicle (UAV) cluster. By constructing the cooperative target allocation model of UAV group, it is ensured that the targets of ultra-deep and large-area river sand bodies are collected. The gradient histogram is used to extract the image characteristics of ultra-deep river sand body and enhance the target image of ultra-deep river sand body. Bi-directional long short-term memory (Bi-LSTM) network model is constructed by introducing bidirectional recurrent neural network (RNN) to improve deep learning. Bi-LSTM neural network is used to construct the target recognition model of ultra-deep river sand body and complete the target recognition. The experimental results show that this method can extract the target edge completely and recognize the image edge accurately, and the average recognition accuracy under different ambiguities is higher than 95. It is proved that this method has high accuracy in sand body feature extraction and classification and has great application potential in river sand body target recognition.