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Silicon widely contained in sand is expected to become a new energy material with environmental protection, safety and low cost. The intelligent management of the exploitation and transportation of such energy resources has become an urgent demand. Aiming at solving the problem of low detection rate of silicon energy bulk material cargo ship targets in river monitoring videos, this paper proposes a multi-ship detection and tracking method based on YOLOv5x combined with DeepSort algorithm. In order to improve the detector recognition efficiency, CIoU Loss is used as the target bounding box regression loss function instead of GIoU Loss to speed up the bounding box regression rate while improving the localization accuracy; NMS is replaced by DIoU-NMS to tackle the problem of missed detection when the targets are dense. The structure of DeepSort appearance feature extraction network is adjusted and trained on a self-built ship dataset to reduce the identity switching caused by target occlusion. The experimental results show that YOLOv5x trained on Pytorch framework makes the model converge fast and accurate with mAP reaching 95.6%, and then combined with DeepSort tracking can achieve the detection and tracking of multiple types of ships. This can provide effective technical support for the supervision of illegal mining and transportation of silicon energy bulk material.