Mr. Hao Jiang Profile

Hao Jiang

at Huangzhou Univ. of Science and Technology

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Proceedings Article | 14 February 2020 Paper

A fast multi-target detection method based on improved YOLO

Xiechang Sun, Hao Jiang, Tongtong Huo, Weidong Yang

Proceedings Volume 11429, 114290Y (2020) https://doi.org/10.1117/12.2539386

KEYWORDS: Target detection, Detection and tracking algorithms, Remote sensing, Performance modeling, Data modeling, Process modeling, Image segmentation, Image processing algorithms and systems, Machine learning, Clouds

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

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