@Article{cmes.2023.028738,
AUTHOR = {Huafeng Chen, Junxing Xue, Hanyun Wen, Yurong Hu, Yudong Zhang},
TITLE = {EfficientShip: A Hybrid Deep Learning Framework for Ship Detection in the River},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {138},
YEAR = {2024},
NUMBER = {1},
PAGES = {301--320},
URL = {http://www.techscience.com/CMES/v138n1/54240},
ISSN = {1526-1506},
ABSTRACT = {Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters. Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection. To solve this problem, we present a hybrid ship detection framework which is named EfficientShip in this paper. The core parts of the EfficientShip are DLA-backboned object location (DBOL) and CascadeRCNN-guided object classification (CROC). The DBOL is responsible for finding potential ship objects, and the CROC is used to categorize the potential ship objects. We also design a pixel-spatial-level data augmentation (PSDA) to reduce the risk of detection model overfitting. We compare the proposed EfficientShip with state-of-the-art (SOTA) literature on a ship detection dataset called Seaships. Experiments show our ship detection framework achieves a result of 99.63% (mAP) at 45 fps, which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.},
DOI = {10.32604/cmes.2023.028738}
}