An effective solution for data enhancement, video enhancement, and target detection algorithm compression is proposed to address the data deficiency, blurred images, and limited computing power of an embedded system of the unmanned underwater vehicle for searching underwater relics. First, the data augmentation was used to improve the generalization ability of small sample, and knowledge transfer was then implemented through transfer learning to accelerate the convergence of models. Then, key frame images were selected from the captured video based on the structural similarity, and the selected key frames were enhanced using the contrast limited adaptive histogram equalization stretching in real time. Finally, based on the multigranularity pruning strategy, YOLOV4 performed bidirectional compression of the channel and convolutional layer. The experimental results show that the operation complexity (BFLOPS) of the compressed YOLOV4 model is reduced to 10.588. The average detection speed for images with a size of 640 pixel×480 pixel on the Jetson TX2 embedded image processor reaches 18.2 frame/s.