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In order to realize the real time detection of Urechis unicinctus burrows in the actual aquaculture pond scene, and provide support for the automatic harvesting and yield prediction of Urechis unicinctus, a deep learning based identification method of Urechis unicinctus burrows was proposed. In view of the limited storage space of the embedded equipment of harvesting vessel and high real time requirements for target detection, the YOLO v4 model had a large number of parameters and a slow detection speed. By replacing the backbone network CSPDarkNet53 of YOLO v4 with a lightweight Mobilenet v2 to reduce the amount of network model parameters and improve the detection speed. On this basis, depthwise separable convolution blocks were used instead of the normal convolution blocks in the Neck and Detection Head parts of the original network to further reduce the number of model parameters. For the poor quality of underwater images, the multi-scale retinex with color restoration (MSRCR) algorithm was selected for image enhancement. Finally, for the original anchor box obtained by clustering the COCO dataset, which was not suitable for small target recognition, the K-means++ algorithm was used to recluster the dataset and optimize the linear scaling of the obtained new anchor box size to obtain the most suitable anchor box for the dataset in order to improve the target detection effect. To simulate the automatic capture scene of Urechis unicinctus, a set of image acquisition equipment with an unmanned ship as the main body was built, and an image data set was established through the collected video to conduct experiments. The trained model deployed on the embedded device Jetson AGX Xavier can detect mean average precision (mAP) of underwater Urechis unicinctus burrows up to 92.26% with detection speed of 36f/s and model size of only 22.2MB. Experiments showed that the method achieved a better balance of detection speed and accuracy and can meet the demand of practical application scenarios where the model was deployed in the embedded equipment of the Urechis unicinctus harvesting vessel. It provided a reference for the subsequent automatic harvesting of Urechis unicinctus and yield prediction in breeding ponds.