Detection of sea surface targets in large-scale remote sensing images is one of the important research topics of oceanremote sensing technology. Ocean remote sensing images have the characteristics of wide format, strong interferenceand small target. This paper adopts the spinning target detection method, and proposes a ship detection model based onYOLO to output the real length, width and axial information. The model can accurately output the position, length andwidth and axial information of a ship target by predicting the minimum external rectangular area of the ship target, so asto realize multi-target detection and improve the detection performance significantly. To improve the recall rate of thetarget detection algorithm, this paper adopts the spinning target detection method, and proposes a ship detection modelbased on YOLO. Through redefining the representation of the rotation matrix and redesigning a new network lossfunction and the rotated IOU computing method, this model accurately outputs the real length, width and axialinformation, increases the output feature dimensions, and effectively raises the recall rate and speed of multi-targetdetection. Lastly, to improve the practicability of the algorithm on mobile devices, the model is processed in alightweight way. Its parameters are significantly reduced while the detection accuracy is ensured.
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