The efficient detection of the underwater biological resources in a complex natural environment is of great significance to China fishery. Aiming at the problems such as low recognition ability and serious feature loss of underwater biological resources in complex low-light environment, a lightweight underwater biological detection algorithm is proposed in this paper. First of all, aiming at the problems of large color deviation and low resolution of underwater images, a Dark channel-contrast limiting-optical attenuation algorithm is proposed to enrich the feature information of underwater images. Thereafter, GhostNet module and C3CA module are used to improve the fusion capability of feature extraction network. Finally, the loss function is improved to reduce the total loss freedom. The experimental results show that the mean average precision of the algorithm reaches 86.22%, which is 0.48% higher than that of the original YOLOv5L. Moreover, the volume of the proposed algorithm model is only 20.4 MB, which is about 89.31% less than that of the original model, and the detection speed of the proposed model is increased by 56.56%. The experimental results show that the improved algorithm achieves good results in underwater images and provides a guarantee for the real-time detection of underwater biological resources.