FA-YOLO: a YOLO model based on attention mechanism and feature fusion for object detection in multi-classification underwater datasets
摘要
In recent years, underwater image object detection has become a hot research topic in computer vision. Due to the unique underwater environment, conventional object detection models tend to perform poorly. Specifically, the attenuation of light in water leads to color distortion and reduced contrast. Suspended particles in water cause scattering, resulting in image blurring and further color issues. Water absorbs different wavelengths of light, with red being the first to disappear, making images predominantly blue or green and severely disrupting color channels. Additionally, the lack of underwater datasets hinders the performance of object detection models in real-world applications, with most datasets being generated by adding noise to existing image datasets for training purposes. Current approaches primarily focus on independent preprocessing of underwater images to enhance their quality. The commonly used models are predominantly two-stage architectures based on RCNN, which improve accuracy at the cost of reduced frame rates. This paper proposes an underwater object detection network based on a single-stage YOLO model. By introducing a coordinate attention mechanism and an improved module based on transformer blocks, the proposed model reduces computational cost while increasing detection frame rates. Experimental results demonstrate that the model achieves both higher accuracy and computational efficiency, with