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A lightweight underwater target detection network CSDP-L-YOLO for channel spatial depth perception is proposed. The network is improved based on the YOLOv5 network and consists of a feature awareness module and a two-attention gating strategy. The feature sensing module aims at adaptive suppression or enhancement of multi-level features in the decoder, optimizing the consistency of in-class learning, and solving the problem of false detection and missing detection caused by the complexity of underwater scenes. The feature mapping is generated by linear operation and mixing structure to reduce the fusion and calculation of redundant features, so as to reduce the number of parameters and calculation amount of the model. The dual attention gating strategy is to introduce concurrent channel space squeezing and stimulation module and convolutional attention module into the encoder at the same time to further focus on the strong correlation features and enhance the sensitivity of the model to the features. The experimental results show that compared with the baseline model, mAP improves by 2.4%, saves 20% parameters and 15.8% computation, and improves the detection speed by 8.2 ms. In addition, mAP improves by 1.9% compared to the current more advanced YOLOv8 model.