A fast multi-target detection method based on improved YOLO

Xiechang Sun; Hao Jiang; Tongtong Huo; Weidong Yang

doi:10.1117/12.2539386

14 February 2020 A fast multi-target detection method based on improved YOLO

Xiechang Sun, Hao Jiang, Tongtong Huo, Weidong Yang

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 114290Y (2020) https://doi.org/10.1117/12.2539386
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

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.

Citation Download Citation

Xiechang Sun, Hao Jiang, Tongtong Huo, and Weidong Yang "A fast multi-target detection method based on improved YOLO", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290Y (14 February 2020); https://doi.org/10.1117/12.2539386

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