Ship detection in infrared images via bounding boxes based on improved YOLOX

Xinjie Qiu; Zhiyu Li; Fenglei Han; Wangyuan Zhao

doi:10.1117/12.2631014

18 March 2022 Ship detection in infrared images via bounding boxes based on improved YOLOX

Xinjie Qiu, Zhiyu Li, Fenglei Han, Wangyuan Zhao

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 1216804 (2022) https://doi.org/10.1117/12.2631014
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

One of the important measures for ship safety is ship detection. Due to the limited visibility at night, it is crucial to detect the ship's infrared night-vision image. We updated the YOLOX to enhance the precision and real-time of ship detection at night, primarily by replacing the CspLayer with a Ghostbottleneck that could adjust to a simple infrared image of the sea environment. Based on the public ship infrared image datasets, our configuration is trained and evaluated. Experiments reveal that, compared to the present algorithm, our method can expedite convergence rate, enhance the F1-score of ship infrared image detection by 0.02, and increase the mPA value by 2.93%.

Citation Download Citation

Xinjie Qiu, Zhiyu Li, Fenglei Han, and Wangyuan Zhao "Ship detection in infrared images via bounding boxes based on improved YOLOX", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 1216804 (18 March 2022); https://doi.org/10.1117/12.2631014

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