TY  - EJOU
AU  - Wu, Wei 
AU  - Li, Xiulai 
AU  - Hu, Zhuhua 
AU  - Liu, Xiaozhang 

TI  - Ship Detection and Recognition Based on Improved YOLOv7
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 76
IS  - 1
SN  - 1546-2226

AB  - In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data augmentation techniques, including random rotation, scaling, and cropping, which serve to bolster data diversity and robustness. The distribution of positive and negative samples in the dataset is balanced using random sampling, ensuring a more accurate representation of real-world scenarios. Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness, highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.
KW  - Ship position prediction; target detection; YOLOv7; data augmentation techniques

DO  - 10.32604/cmc.2023.039929