## 2 Related Works  
### Tiny Object Detection in Maritime Scenarios  
The significant difference in pixel intensity values between tiny objects and background is crucial in object detection based on visual saliency. The method based on pixel intensity difference is generally applied to single-channel infrared images and rarely to grayscale visible images. Compared with maritime scenarios, the background in field scenarios is relatively complicated, which leads to a low signal-to-noise ratio around tiny objects, which in turn makes it difficult to distinguish objects from strong noise and to use the saliency-based method to obtain excellent performance. In maritime UAV-captured images, sun glint, waves, and haze can diminish the saliency of objects, while flat background and monochromatic characteristics inherently enhance this characteristic in tiny objects with distinctive colors. Thus, under favorable conditions, maritime tiny human object detection can be performed based on visual saliency and the analysis of background and object characteristics. Generally, research on object detection is based on the local contrast measurement mechanism (LCM) [32] and its derivative methods [16; 33; 34; 35]. The local differences in pixel intensity values between objects and the background in grayscale imagery are analyzed to highlight the saliency of objects; then, object localization can be performed by setting a threshold. The methods based on visual saliency are generally applied in infrared images with flat backgrounds. However, it is difficult to limit the high probability of false alarms caused by sun glint [36; 37]. Multi-scale average absolute gray difference (MS-AAGD) and  
Figure 1: Tiny human target detection method for maritime SAR based on visual saliency (HDetect-VS). Laplacian of point spread function (LoPSF) are utilized to suppress false alarm sources [38]. However, when object signals are concealed by strong noise, it is more difficult to detect them based on pixel intensity difference. Therefore, in maritime scenarios, it is much more difficult to localize tiny human targets in noisy infrared images, while target detection can be achieved in visible images with rich details. Deep learning-based methods are well suited for real-time object detection, with excellent performance in generic scenarios. In images captured by UAVs, high-resolution characteristics and tiny-sized objects complicate object detection. Under limitations of computing and time resources, it is difficult for deep learning-based algorithms to directly train models based on high-resolution images. Generic algorithms scale the input images to a fixed size to reduce computing and time costs, which inevitably causes a reduction in the detail level of features. Moreover, the difference between the theoretical receptive field and the effective receptive field is intensified for tiny objects, further leading to the reduction in positive target samples [39; 40]. Genetic models [41] show much better performance on large and medium objects than on tiny objects. To improve performance in tiny object detection, data augmentation [42; 43; 44; 45] and feature fusion [46; 47] have been proposed to enhance the learning and presentation of features. However, the details that can be learned in the receptive field for tiny objects are insufficient; therefore, the problem of poor and weak features cannot be effectively overcome. A solution is represented by generative adversarial networks (GANs) [47], which transform the original poor and weak features of tiny objects into highly discriminative ones by introducing fine-grained details from lower-level layers. In tiny object detection tasks in maritime SAR, the negative effect of sun glint on object texture needs to be addressed. When objects are concealed by sun glint, model performance is significantly poorer due to their weak features. In real-world SAR tasks, it is more important to find objects reliably, rather than accurately localizing them [48].