TY  - EJOU
AU  - Chen, Huafeng 
AU  - Xue, Junxing 
AU  - Wen, Hanyun 
AU  - Hu, Yurong 
AU  - Zhang, Yudong 

TI  - EfficientShip: A Hybrid Deep Learning Framework for Ship Detection in the River
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 138
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Ship detection; deep learning; data augmentation; object location; object classification

DO  - 10.32604/cmes.2023.028738